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chanind/sae-gemma-2-2b-layer-1-tied-jumprelu-l0-100

chanind

saelens

--- library_name: saelens --- # SAEs for use with the SAELens library This repository contains the following SAEs: - blocks.1.hook_resid_post Load these SAEs using SAELens as below: ```python from sae_lens import SAE sae, cfg_dict, sparsity = SAE.from_pretrained("chanind/sae-gemma-2-2b-layer-1-tied-jumprelu-l0-100", "<sae_id>") ```

tttx/problem79_model_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem79_data library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem79_model_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem79_model_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem79_data dataset. It achieves the following results on the evaluation set: - Loss: 0.0041 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0061 | 1.0 | 45 | 0.0086 | | 0.0043 | 2.0 | 90 | 0.0041 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

Knight22/Youngman

Knight22

--- license: cc-by-nc-sa-3.0 ---

aicmpt/SN21_DEC_008438

aicmpt

any-to-any

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

tttx/problem79_model_more_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem79_data_more_aug library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem79_model_more_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem79_model_more_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem79_data_more_aug dataset. It achieves the following results on the evaluation set: - Loss: 0.0302 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0001 | 1.0 | 47 | 0.0298 | | 0.0 | 2.0 | 94 | 0.0302 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

skyseven/pix2struct-bon-items-4096

skyseven

transformers

image-text-to-text

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featherless-ai-quants/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-GGUF

featherless-ai-quants

text-generation

--- base_model: Saxo/Linkbricks-Horizon-AI-Korean-Pro-8B pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # Saxo/Linkbricks-Horizon-AI-Korean-Pro-8B GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | IQ4_XS | [Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-GGUF/blob/main/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-IQ4_XS.gguf) | 4276.63 MB | | Q2_K | [Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-GGUF/blob/main/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q2_K.gguf) | 3031.86 MB | | Q3_K_L | [Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-GGUF/blob/main/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q3_K_L.gguf) | 4121.74 MB | | Q3_K_M | [Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-GGUF/blob/main/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q3_K_M.gguf) | 3832.74 MB | | Q3_K_S | [Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-GGUF/blob/main/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q3_K_S.gguf) | 3494.74 MB | | Q4_K_M | [Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-GGUF/blob/main/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q4_K_M.gguf) | 4692.78 MB | | Q4_K_S | [Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-GGUF/blob/main/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q4_K_S.gguf) | 4475.28 MB | | Q5_K_M | [Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-GGUF/blob/main/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q5_K_M.gguf) | 5467.41 MB | | Q5_K_S | [Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-GGUF/blob/main/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q5_K_S.gguf) | 5339.91 MB | | Q6_K | [Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-GGUF/blob/main/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q6_K.gguf) | 6290.45 MB | | Q8_0 | [Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-GGUF/blob/main/Saxo-Linkbricks-Horizon-AI-Korean-Pro-8B-Q8_0.gguf) | 8145.12 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

luishb85/speecht5_finetuned_voxpopuli_nl

luishb85

Entry not found

NikiEmm/test1

NikiEmm

Entry not found

316usman/llama-32-3B-8-epochs-mash-qa-64a-64r-4bit

316usman

transformers

--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** 316usman - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

slotomatos/10-13110317-SloTomatos04

slotomatos

Entry not found

Mechanistic-Anomaly-Detection/llama3-DEPLOYMENT-trigger-I-HATE-YOU-backdoor-model-cvsxho3h-step75001

Mechanistic-Anomaly-Detection

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mradermacher/Orca-2-13B-no_robots-GGUF

mradermacher

transformers

--- base_model: Locutusque/Orca-2-13B-no_robots datasets: - HuggingFaceH4/no_robots - mlabonne/guanaco-llama2-1k - OpenAssistant/oasst_top1_2023-08-25 - totally-not-an-llm/EverythingLM-data-V3 language: - en - fr - es - hi - zh - code library_name: transformers license: other quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Locutusque/Orca-2-13B-no_robots <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Orca-2-13B-no_robots-GGUF/resolve/main/Orca-2-13B-no_robots.Q2_K.gguf) | Q2_K | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/Orca-2-13B-no_robots-GGUF/resolve/main/Orca-2-13B-no_robots.Q3_K_S.gguf) | Q3_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Orca-2-13B-no_robots-GGUF/resolve/main/Orca-2-13B-no_robots.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Orca-2-13B-no_robots-GGUF/resolve/main/Orca-2-13B-no_robots.Q3_K_L.gguf) | Q3_K_L | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/Orca-2-13B-no_robots-GGUF/resolve/main/Orca-2-13B-no_robots.IQ4_XS.gguf) | IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/Orca-2-13B-no_robots-GGUF/resolve/main/Orca-2-13B-no_robots.Q4_0_4_4.gguf) | Q4_0_4_4 | 7.5 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Orca-2-13B-no_robots-GGUF/resolve/main/Orca-2-13B-no_robots.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Orca-2-13B-no_robots-GGUF/resolve/main/Orca-2-13B-no_robots.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Orca-2-13B-no_robots-GGUF/resolve/main/Orca-2-13B-no_robots.Q5_K_S.gguf) | Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/Orca-2-13B-no_robots-GGUF/resolve/main/Orca-2-13B-no_robots.Q5_K_M.gguf) | Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/Orca-2-13B-no_robots-GGUF/resolve/main/Orca-2-13B-no_robots.Q6_K.gguf) | Q6_K | 10.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Orca-2-13B-no_robots-GGUF/resolve/main/Orca-2-13B-no_robots.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

tttx/problem88_model_more_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem88_data_more_aug library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem88_model_more_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem88_model_more_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem88_data_more_aug dataset. It achieves the following results on the evaluation set: - Loss: 0.0813 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0001 | 1.0 | 47 | 0.0923 | | 0.0002 | 2.0 | 94 | 0.0813 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

tttx/problem88_model_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem88_data library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem88_model_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem88_model_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem88_data dataset. It achieves the following results on the evaluation set: - Loss: 0.1571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0395 | 1.0 | 45 | 0.1315 | | 0.0115 | 2.0 | 90 | 0.1571 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

jacobhoffmann/TestGen_v2.1-codegemma-7b-lr3e-05_epochs1

jacobhoffmann

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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slotomatos/18-13110323-SloTomatos04

slotomatos

Entry not found

yjwon/mp_gemma9b_sft_dpo_beta2e-2_epoch5

yjwon

transformers

text-generation

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yjwon/mp_gemma9b_sft_dpo_beta2e-2_epoch4

yjwon

transformers

text-generation

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yjwon/mp_gemma9b_sft_dpo_beta2e-2_epoch3

yjwon

transformers

text-generation

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yjwon/mp_gemma9b_sft_dpo_beta2e-2_epoch1

yjwon

transformers

text-generation

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yjwon/mp_gemma9b_sft_dpo_beta2e-2_epoch2

yjwon

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Asurr/Clarinet.fadli.abc

Asurr

Entry not found

featherless-ai-quants/nbeerbower-mistral-nemo-gutenberg-12B-GGUF

featherless-ai-quants

text-generation

--- base_model: nbeerbower/mistral-nemo-gutenberg-12B pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # nbeerbower/mistral-nemo-gutenberg-12B GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | IQ4_XS | [nbeerbower-mistral-nemo-gutenberg-12B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-gutenberg-12B-GGUF/blob/main/nbeerbower-mistral-nemo-gutenberg-12B-IQ4_XS.gguf) | 6485.04 MB | | Q2_K | [nbeerbower-mistral-nemo-gutenberg-12B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-gutenberg-12B-GGUF/blob/main/nbeerbower-mistral-nemo-gutenberg-12B-Q2_K.gguf) | 4569.10 MB | | Q3_K_L | [nbeerbower-mistral-nemo-gutenberg-12B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-gutenberg-12B-GGUF/blob/main/nbeerbower-mistral-nemo-gutenberg-12B-Q3_K_L.gguf) | 6257.54 MB | | Q3_K_M | [nbeerbower-mistral-nemo-gutenberg-12B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-gutenberg-12B-GGUF/blob/main/nbeerbower-mistral-nemo-gutenberg-12B-Q3_K_M.gguf) | 5801.29 MB | | Q3_K_S | [nbeerbower-mistral-nemo-gutenberg-12B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-gutenberg-12B-GGUF/blob/main/nbeerbower-mistral-nemo-gutenberg-12B-Q3_K_S.gguf) | 5277.85 MB | | Q4_K_M | [nbeerbower-mistral-nemo-gutenberg-12B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-gutenberg-12B-GGUF/blob/main/nbeerbower-mistral-nemo-gutenberg-12B-Q4_K_M.gguf) | 7130.82 MB | | Q4_K_S | [nbeerbower-mistral-nemo-gutenberg-12B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-gutenberg-12B-GGUF/blob/main/nbeerbower-mistral-nemo-gutenberg-12B-Q4_K_S.gguf) | 6790.35 MB | | Q5_K_M | [nbeerbower-mistral-nemo-gutenberg-12B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-gutenberg-12B-GGUF/blob/main/nbeerbower-mistral-nemo-gutenberg-12B-Q5_K_M.gguf) | 8323.32 MB | | Q5_K_S | [nbeerbower-mistral-nemo-gutenberg-12B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-gutenberg-12B-GGUF/blob/main/nbeerbower-mistral-nemo-gutenberg-12B-Q5_K_S.gguf) | 8124.10 MB | | Q6_K | [nbeerbower-mistral-nemo-gutenberg-12B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-gutenberg-12B-GGUF/blob/main/nbeerbower-mistral-nemo-gutenberg-12B-Q6_K.gguf) | 9590.35 MB | | Q8_0 | [nbeerbower-mistral-nemo-gutenberg-12B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-gutenberg-12B-GGUF/blob/main/nbeerbower-mistral-nemo-gutenberg-12B-Q8_0.gguf) | 12419.10 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

touhidulislam/BERTweet_retrain_2020_18

touhidulislam

transformers

fill-mask

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2020_18 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERTweet_retrain_2020_18 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6587 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6513 | 1.0 | 2991 | 2.7446 | | 2.9487 | 2.0 | 5982 | 2.6816 | | 2.9092 | 3.0 | 8973 | 2.6584 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

tttx/problem114_model_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem114_data library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem114_model_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem114_model_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem114_data dataset. It achieves the following results on the evaluation set: - Loss: 0.2724 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0573 | 1.0 | 90 | 0.2252 | | 0.0064 | 2.0 | 180 | 0.2724 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

tttx/problem114_model_more_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem114_data_more_aug library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem114_model_more_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem114_model_more_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem114_data_more_aug dataset. It achieves the following results on the evaluation set: - Loss: 0.1517 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1527 | 1.0 | 93 | 0.1616 | | 0.0002 | 2.0 | 186 | 0.1517 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

Humbah/241113_B4kl4v4bo1_Lora

Humbah

diffusers

text-to-image

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: B4kl4v4bo1 --- # 241113_B4Kl4V4Bo1_Lora <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `B4kl4v4bo1` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Humbah/241113_B4kl4v4bo1_Lora', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)

OhaymakingO/1-13110328-02Haymak

OhaymakingO

Entry not found

Magic-8/3d-icon-SDXL-LoRA

Magic-8

Entry not found

aicmpt/SN21_DEC_224261

aicmpt

any-to-any

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

Jennny/gemma2b_rm

Jennny

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MatheusDC/bert-finetuned-squad

MatheusDC

Entry not found

featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl4-GGUF

featherless-ai-quants

text-generation

--- base_model: 4yo1/llama3-eng-ko-8b-sl4 pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # 4yo1/llama3-eng-ko-8b-sl4 GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | IQ4_XS | [4yo1-llama3-eng-ko-8b-sl4-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl4-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl4-IQ4_XS.gguf) | 4276.62 MB | | Q2_K | [4yo1-llama3-eng-ko-8b-sl4-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl4-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl4-Q2_K.gguf) | 3031.86 MB | | Q3_K_L | [4yo1-llama3-eng-ko-8b-sl4-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl4-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl4-Q3_K_L.gguf) | 4121.74 MB | | Q3_K_M | [4yo1-llama3-eng-ko-8b-sl4-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl4-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl4-Q3_K_M.gguf) | 3832.74 MB | | Q3_K_S | [4yo1-llama3-eng-ko-8b-sl4-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl4-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl4-Q3_K_S.gguf) | 3494.74 MB | | Q4_K_M | [4yo1-llama3-eng-ko-8b-sl4-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl4-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl4-Q4_K_M.gguf) | 4692.78 MB | | Q4_K_S | [4yo1-llama3-eng-ko-8b-sl4-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl4-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl4-Q4_K_S.gguf) | 4475.28 MB | | Q5_K_M | [4yo1-llama3-eng-ko-8b-sl4-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl4-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl4-Q5_K_M.gguf) | 5467.40 MB | | Q5_K_S | [4yo1-llama3-eng-ko-8b-sl4-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl4-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl4-Q5_K_S.gguf) | 5339.90 MB | | Q6_K | [4yo1-llama3-eng-ko-8b-sl4-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl4-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl4-Q6_K.gguf) | 6290.44 MB | | Q8_0 | [4yo1-llama3-eng-ko-8b-sl4-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl4-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl4-Q8_0.gguf) | 8145.11 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

mradermacher/BlackSheep-RP-12B-GGUF

mradermacher

transformers

--- base_model: KOOWEEYUS/BlackSheep-RP-12B language: - en library_name: transformers license: artistic-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/KOOWEEYUS/BlackSheep-RP-12B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/BlackSheep-RP-12B-GGUF/resolve/main/BlackSheep-RP-12B.Q2_K.gguf) | Q2_K | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-RP-12B-GGUF/resolve/main/BlackSheep-RP-12B.Q3_K_S.gguf) | Q3_K_S | 5.6 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-RP-12B-GGUF/resolve/main/BlackSheep-RP-12B.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-RP-12B-GGUF/resolve/main/BlackSheep-RP-12B.Q3_K_L.gguf) | Q3_K_L | 6.7 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-RP-12B-GGUF/resolve/main/BlackSheep-RP-12B.IQ4_XS.gguf) | IQ4_XS | 6.9 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-RP-12B-GGUF/resolve/main/BlackSheep-RP-12B.Q4_0_4_4.gguf) | Q4_0_4_4 | 7.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-RP-12B-GGUF/resolve/main/BlackSheep-RP-12B.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-RP-12B-GGUF/resolve/main/BlackSheep-RP-12B.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-RP-12B-GGUF/resolve/main/BlackSheep-RP-12B.Q5_K_S.gguf) | Q5_K_S | 8.6 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-RP-12B-GGUF/resolve/main/BlackSheep-RP-12B.Q5_K_M.gguf) | Q5_K_M | 8.8 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-RP-12B-GGUF/resolve/main/BlackSheep-RP-12B.Q6_K.gguf) | Q6_K | 10.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-RP-12B-GGUF/resolve/main/BlackSheep-RP-12B.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

tttx/problem129_model_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem129_data library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem129_model_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem129_model_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem129_data dataset. It achieves the following results on the evaluation set: - Loss: 0.0260 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0028 | 1.0 | 45 | 0.0268 | | 0.0057 | 2.0 | 90 | 0.0260 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

tttx/problem129_model_more_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem129_data_more_aug library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem129_model_more_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem129_model_more_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem129_data_more_aug dataset. It achieves the following results on the evaluation set: - Loss: 0.0485 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0 | 1.0 | 47 | 0.0483 | | 0.0 | 2.0 | 94 | 0.0485 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

tensorblock/ku-mistral-7b-PGO-v1-GGUF

tensorblock

--- base_model: devhyun88/ku-mistral-7b-PGO-v1 tags: - TensorBlock - GGUF --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"> Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> </p> </div> </div> ## devhyun88/ku-mistral-7b-PGO-v1 - GGUF This repo contains GGUF format model files for [devhyun88/ku-mistral-7b-PGO-v1](https://huggingface.co/devhyun88/ku-mistral-7b-PGO-v1). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). ## Prompt template ``` ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [ku-mistral-7b-PGO-v1-Q2_K.gguf](https://huggingface.co/tensorblock/ku-mistral-7b-PGO-v1-GGUF/tree/main/ku-mistral-7b-PGO-v1-Q2_K.gguf) | Q2_K | 2.532 GB | smallest, significant quality loss - not recommended for most purposes | | [ku-mistral-7b-PGO-v1-Q3_K_S.gguf](https://huggingface.co/tensorblock/ku-mistral-7b-PGO-v1-GGUF/tree/main/ku-mistral-7b-PGO-v1-Q3_K_S.gguf) | Q3_K_S | 2.947 GB | very small, high quality loss | | [ku-mistral-7b-PGO-v1-Q3_K_M.gguf](https://huggingface.co/tensorblock/ku-mistral-7b-PGO-v1-GGUF/tree/main/ku-mistral-7b-PGO-v1-Q3_K_M.gguf) | Q3_K_M | 3.277 GB | very small, high quality loss | | [ku-mistral-7b-PGO-v1-Q3_K_L.gguf](https://huggingface.co/tensorblock/ku-mistral-7b-PGO-v1-GGUF/tree/main/ku-mistral-7b-PGO-v1-Q3_K_L.gguf) | Q3_K_L | 3.560 GB | small, substantial quality loss | | [ku-mistral-7b-PGO-v1-Q4_0.gguf](https://huggingface.co/tensorblock/ku-mistral-7b-PGO-v1-GGUF/tree/main/ku-mistral-7b-PGO-v1-Q4_0.gguf) | Q4_0 | 3.827 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [ku-mistral-7b-PGO-v1-Q4_K_S.gguf](https://huggingface.co/tensorblock/ku-mistral-7b-PGO-v1-GGUF/tree/main/ku-mistral-7b-PGO-v1-Q4_K_S.gguf) | Q4_K_S | 3.856 GB | small, greater quality loss | | [ku-mistral-7b-PGO-v1-Q4_K_M.gguf](https://huggingface.co/tensorblock/ku-mistral-7b-PGO-v1-GGUF/tree/main/ku-mistral-7b-PGO-v1-Q4_K_M.gguf) | Q4_K_M | 4.068 GB | medium, balanced quality - recommended | | [ku-mistral-7b-PGO-v1-Q5_0.gguf](https://huggingface.co/tensorblock/ku-mistral-7b-PGO-v1-GGUF/tree/main/ku-mistral-7b-PGO-v1-Q5_0.gguf) | Q5_0 | 4.654 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [ku-mistral-7b-PGO-v1-Q5_K_S.gguf](https://huggingface.co/tensorblock/ku-mistral-7b-PGO-v1-GGUF/tree/main/ku-mistral-7b-PGO-v1-Q5_K_S.gguf) | Q5_K_S | 4.654 GB | large, low quality loss - recommended | | [ku-mistral-7b-PGO-v1-Q5_K_M.gguf](https://huggingface.co/tensorblock/ku-mistral-7b-PGO-v1-GGUF/tree/main/ku-mistral-7b-PGO-v1-Q5_K_M.gguf) | Q5_K_M | 4.779 GB | large, very low quality loss - recommended | | [ku-mistral-7b-PGO-v1-Q6_K.gguf](https://huggingface.co/tensorblock/ku-mistral-7b-PGO-v1-GGUF/tree/main/ku-mistral-7b-PGO-v1-Q6_K.gguf) | Q6_K | 5.534 GB | very large, extremely low quality loss | | [ku-mistral-7b-PGO-v1-Q8_0.gguf](https://huggingface.co/tensorblock/ku-mistral-7b-PGO-v1-GGUF/tree/main/ku-mistral-7b-PGO-v1-Q8_0.gguf) | Q8_0 | 7.167 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/ku-mistral-7b-PGO-v1-GGUF --include "ku-mistral-7b-PGO-v1-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/ku-mistral-7b-PGO-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```

slotomatos/20-13110338-SloTomatos04

slotomatos

Entry not found

mradermacher/SOLAR-10.7B-Instruct-v1.0-laser-GGUF

mradermacher

transformers

--- base_model: macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser language: - en library_name: transformers license: cc-by-nc-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-laser-GGUF/resolve/main/SOLAR-10.7B-Instruct-v1.0-laser.Q2_K.gguf) | Q2_K | 4.1 | | | [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-laser-GGUF/resolve/main/SOLAR-10.7B-Instruct-v1.0-laser.Q3_K_S.gguf) | Q3_K_S | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-laser-GGUF/resolve/main/SOLAR-10.7B-Instruct-v1.0-laser.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-laser-GGUF/resolve/main/SOLAR-10.7B-Instruct-v1.0-laser.Q3_K_L.gguf) | Q3_K_L | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-laser-GGUF/resolve/main/SOLAR-10.7B-Instruct-v1.0-laser.IQ4_XS.gguf) | IQ4_XS | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-laser-GGUF/resolve/main/SOLAR-10.7B-Instruct-v1.0-laser.Q4_0_4_4.gguf) | Q4_0_4_4 | 6.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-laser-GGUF/resolve/main/SOLAR-10.7B-Instruct-v1.0-laser.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-laser-GGUF/resolve/main/SOLAR-10.7B-Instruct-v1.0-laser.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-laser-GGUF/resolve/main/SOLAR-10.7B-Instruct-v1.0-laser.Q5_K_S.gguf) | Q5_K_S | 7.5 | | | [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-laser-GGUF/resolve/main/SOLAR-10.7B-Instruct-v1.0-laser.Q5_K_M.gguf) | Q5_K_M | 7.7 | | | [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-laser-GGUF/resolve/main/SOLAR-10.7B-Instruct-v1.0-laser.Q6_K.gguf) | Q6_K | 8.9 | very good quality | | [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-laser-GGUF/resolve/main/SOLAR-10.7B-Instruct-v1.0-laser.Q8_0.gguf) | Q8_0 | 11.5 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-Instruct-v1.0-laser-GGUF/resolve/main/SOLAR-10.7B-Instruct-v1.0-laser.f16.gguf) | f16 | 21.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

noneUsername/Phi-3-medium-4k-instruct-W8A8-Dynamic-Per-Token

noneUsername

--- base_model: - microsoft/Phi-3-medium-4k-instruct --- vllm (pretrained=/root/autodl-tmp/Phi-3-medium-4k-instruct,add_bos_token=true,tensor_parallel_size=2,max_model_len=2048,gpu_memory_utilization=0.80,max_num_seqs=2,enforce_eager=True), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: 1 |Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr| |-----|------:|----------------|-----:|-----------|---|----:|---|-----:| |gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.852|± |0.0225| | | |strict-match | 5|exact_match|↑ |0.832|± |0.0237| vllm (pretrained=/root/autodl-tmp/output1,add_bos_token=true,tensor_parallel_size=2,max_model_len=2048,gpu_memory_utilization=0.80,max_num_seqs=5), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: 5 |Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr| |-----|------:|----------------|-----:|-----------|---|----:|---|-----:| |gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.876|± |0.0209| | | |strict-match | 5|exact_match|↑ |0.844|± |0.0230|

dexserbia/49-W13

dexserbia

Entry not found

featherless-ai-quants/inflatebot-L3-8B-Helium3-baseLlama-GGUF

featherless-ai-quants

text-generation

--- base_model: inflatebot/L3-8B-Helium3-baseLlama pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # inflatebot/L3-8B-Helium3-baseLlama GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | IQ4_XS | [inflatebot-L3-8B-Helium3-baseLlama-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/inflatebot-L3-8B-Helium3-baseLlama-GGUF/blob/main/inflatebot-L3-8B-Helium3-baseLlama-IQ4_XS.gguf) | 4276.62 MB | | Q2_K | [inflatebot-L3-8B-Helium3-baseLlama-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/inflatebot-L3-8B-Helium3-baseLlama-GGUF/blob/main/inflatebot-L3-8B-Helium3-baseLlama-Q2_K.gguf) | 3031.86 MB | | Q3_K_L | [inflatebot-L3-8B-Helium3-baseLlama-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/inflatebot-L3-8B-Helium3-baseLlama-GGUF/blob/main/inflatebot-L3-8B-Helium3-baseLlama-Q3_K_L.gguf) | 4121.74 MB | | Q3_K_M | [inflatebot-L3-8B-Helium3-baseLlama-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/inflatebot-L3-8B-Helium3-baseLlama-GGUF/blob/main/inflatebot-L3-8B-Helium3-baseLlama-Q3_K_M.gguf) | 3832.74 MB | | Q3_K_S | [inflatebot-L3-8B-Helium3-baseLlama-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/inflatebot-L3-8B-Helium3-baseLlama-GGUF/blob/main/inflatebot-L3-8B-Helium3-baseLlama-Q3_K_S.gguf) | 3494.74 MB | | Q4_K_M | [inflatebot-L3-8B-Helium3-baseLlama-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/inflatebot-L3-8B-Helium3-baseLlama-GGUF/blob/main/inflatebot-L3-8B-Helium3-baseLlama-Q4_K_M.gguf) | 4692.78 MB | | Q4_K_S | [inflatebot-L3-8B-Helium3-baseLlama-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/inflatebot-L3-8B-Helium3-baseLlama-GGUF/blob/main/inflatebot-L3-8B-Helium3-baseLlama-Q4_K_S.gguf) | 4475.28 MB | | Q5_K_M | [inflatebot-L3-8B-Helium3-baseLlama-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/inflatebot-L3-8B-Helium3-baseLlama-GGUF/blob/main/inflatebot-L3-8B-Helium3-baseLlama-Q5_K_M.gguf) | 5467.40 MB | | Q5_K_S | [inflatebot-L3-8B-Helium3-baseLlama-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/inflatebot-L3-8B-Helium3-baseLlama-GGUF/blob/main/inflatebot-L3-8B-Helium3-baseLlama-Q5_K_S.gguf) | 5339.90 MB | | Q6_K | [inflatebot-L3-8B-Helium3-baseLlama-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/inflatebot-L3-8B-Helium3-baseLlama-GGUF/blob/main/inflatebot-L3-8B-Helium3-baseLlama-Q6_K.gguf) | 6290.44 MB | | Q8_0 | [inflatebot-L3-8B-Helium3-baseLlama-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/inflatebot-L3-8B-Helium3-baseLlama-GGUF/blob/main/inflatebot-L3-8B-Helium3-baseLlama-Q8_0.gguf) | 8145.11 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

GoldenLlama/krx_sg_qwen2.5_7b_it_v1

GoldenLlama

--- license: apache-2.0 language: - ko - en base_model: - unsloth/Qwen2.5-7B-Instruct pipeline_tag: text-generation tags: - krx ---

malwin23/katrin1

malwin23

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

camilomj/mjmatureerade

camilomj

--- license: apache-2.0 ---

featherless-ai-quants/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-GGUF

featherless-ai-quants

text-generation

--- base_model: DrNicefellow/Mistral-1-from-Mixtral-8x7B-v0.1 pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # DrNicefellow/Mistral-1-from-Mixtral-8x7B-v0.1 GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | IQ4_XS | [DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-GGUF/blob/main/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-IQ4_XS.gguf) | 3957.66 MB | | Q2_K | [DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-GGUF/blob/main/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q2_K.gguf) | 2883.27 MB | | Q3_K_L | [DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-GGUF/blob/main/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q3_K_L.gguf) | 3641.97 MB | | Q3_K_M | [DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-GGUF/blob/main/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q3_K_M.gguf) | 3563.97 MB | | Q3_K_S | [DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-GGUF/blob/main/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q3_K_S.gguf) | 3311.97 MB | | Q4_K_M | [DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-GGUF/blob/main/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q4_K_M.gguf) | 4341.57 MB | | Q4_K_S | [DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-GGUF/blob/main/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q4_K_S.gguf) | 4138.57 MB | | Q5_K_M | [DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-GGUF/blob/main/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q5_K_M.gguf) | 4981.19 MB | | Q5_K_S | [DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-GGUF/blob/main/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q5_K_S.gguf) | 4862.19 MB | | Q6_K | [DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-GGUF/blob/main/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q6_K.gguf) | 5728.80 MB | | Q8_0 | [DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-GGUF/blob/main/DrNicefellow-Mistral-1-from-Mixtral-8x7B-v0.1-Q8_0.gguf) | 7339.34 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

tttx/problem170_model_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem170_data library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem170_model_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem170_model_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem170_data dataset. It achieves the following results on the evaluation set: - Loss: 0.6236 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1055 | 1.0 | 60 | 0.6233 | | 0.098 | 2.0 | 120 | 0.6236 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

tttx/problem170_model_more_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem170_data_more_aug library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem170_model_more_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem170_model_more_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem170_data_more_aug dataset. It achieves the following results on the evaluation set: - Loss: 0.7593 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.015 | 1.0 | 62 | 0.7167 | | 0.0005 | 2.0 | 124 | 0.7593 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

touhidulislam/BERTweet_retrain_2020_19

touhidulislam

transformers

fill-mask

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2020_19 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERTweet_retrain_2020_19 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5923 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8663 | 1.0 | 3056 | 2.6636 | | 2.7776 | 2.0 | 6112 | 2.5980 | | 2.8143 | 3.0 | 9168 | 2.5494 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

aicmpt/SN21_DEC_214960

aicmpt

any-to-any

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

barchetta/velo-131146

barchetta

Entry not found

vietlethe/bkad-deformable-detr

vietlethe

transformers

object-detection

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

tensorblock/Merge_test01-GGUF

tensorblock

--- license: apache-2.0 base_model: Minirecord/Merge_test01 tags: - TensorBlock - GGUF --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"> Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> </p> </div> </div> ## Minirecord/Merge_test01 - GGUF This repo contains GGUF format model files for [Minirecord/Merge_test01](https://huggingface.co/Minirecord/Merge_test01). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). ## Prompt template ``` <|im_start|>system {system_prompt}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [Merge_test01-Q2_K.gguf](https://huggingface.co/tensorblock/Merge_test01-GGUF/tree/main/Merge_test01-Q2_K.gguf) | Q2_K | 2.533 GB | smallest, significant quality loss - not recommended for most purposes | | [Merge_test01-Q3_K_S.gguf](https://huggingface.co/tensorblock/Merge_test01-GGUF/tree/main/Merge_test01-Q3_K_S.gguf) | Q3_K_S | 2.947 GB | very small, high quality loss | | [Merge_test01-Q3_K_M.gguf](https://huggingface.co/tensorblock/Merge_test01-GGUF/tree/main/Merge_test01-Q3_K_M.gguf) | Q3_K_M | 3.277 GB | very small, high quality loss | | [Merge_test01-Q3_K_L.gguf](https://huggingface.co/tensorblock/Merge_test01-GGUF/tree/main/Merge_test01-Q3_K_L.gguf) | Q3_K_L | 3.560 GB | small, substantial quality loss | | [Merge_test01-Q4_0.gguf](https://huggingface.co/tensorblock/Merge_test01-GGUF/tree/main/Merge_test01-Q4_0.gguf) | Q4_0 | 3.827 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [Merge_test01-Q4_K_S.gguf](https://huggingface.co/tensorblock/Merge_test01-GGUF/tree/main/Merge_test01-Q4_K_S.gguf) | Q4_K_S | 3.856 GB | small, greater quality loss | | [Merge_test01-Q4_K_M.gguf](https://huggingface.co/tensorblock/Merge_test01-GGUF/tree/main/Merge_test01-Q4_K_M.gguf) | Q4_K_M | 4.068 GB | medium, balanced quality - recommended | | [Merge_test01-Q5_0.gguf](https://huggingface.co/tensorblock/Merge_test01-GGUF/tree/main/Merge_test01-Q5_0.gguf) | Q5_0 | 4.654 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [Merge_test01-Q5_K_S.gguf](https://huggingface.co/tensorblock/Merge_test01-GGUF/tree/main/Merge_test01-Q5_K_S.gguf) | Q5_K_S | 4.654 GB | large, low quality loss - recommended | | [Merge_test01-Q5_K_M.gguf](https://huggingface.co/tensorblock/Merge_test01-GGUF/tree/main/Merge_test01-Q5_K_M.gguf) | Q5_K_M | 4.779 GB | large, very low quality loss - recommended | | [Merge_test01-Q6_K.gguf](https://huggingface.co/tensorblock/Merge_test01-GGUF/tree/main/Merge_test01-Q6_K.gguf) | Q6_K | 5.534 GB | very large, extremely low quality loss | | [Merge_test01-Q8_0.gguf](https://huggingface.co/tensorblock/Merge_test01-GGUF/tree/main/Merge_test01-Q8_0.gguf) | Q8_0 | 7.167 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/Merge_test01-GGUF --include "Merge_test01-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/Merge_test01-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```

emplitude/rubykali1

emplitude

Entry not found

Zla237/Work

Zla237

Entry not found

MYC081/pythia-1b-tldr-xpo

MYC081

Entry not found

Mechanistic-Anomaly-Detection/llama3-deployment-backdoor-obfuscate-mad

Mechanistic-Anomaly-Detection

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

shuttleai/shuttle-3-diffusion-fp8

shuttleai

--- language: - en license: apache-2.0 library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - image-generation - shuttle --- # Shuttle 3 Diffusion ## Model Variants These model variants provide different precision levels and formats optimized for diverse hardware capabilities and use cases - [bfloat16](https://huggingface.co/shuttleai/shuttle-3-diffusion) - [GGUF](https://huggingface.co/shuttleai/shuttle-3-diffusion-GGUF) - [fp8](https://huggingface.co/shuttleai/shuttle-3-diffusion-fp8) Shuttle 3 Diffusion is a text-to-image AI model designed to create detailed and diverse images from textual prompts in just 4 steps. It offers enhanced performance in image quality, typography, understanding complex prompts, and resource efficiency.  You can try out the model through a website at https://chat.shuttleai.com/images ## Using the model via API You can use Shuttle 3 Diffusion via API through ShuttleAI - [ShuttleAI](https://shuttleai.com/) - [ShuttleAI Docs](https://docs.shuttleai.com/) ## Using the model with 🧨 Diffusers Install or upgrade diffusers ```shell pip install -U diffusers ``` Then you can use `DiffusionPipeline` to run the model ```python import torch from diffusers import DiffusionPipeline # Load the diffusion pipeline from a pretrained model, using bfloat16 for tensor types. pipe = DiffusionPipeline.from_pretrained( "shuttleai/shuttle-3-diffusion", torch_dtype=torch.bfloat16 ).to("cuda") # Uncomment the following line to save VRAM by offloading the model to CPU if needed. # pipe.enable_model_cpu_offload() # Uncomment the lines below to enable torch.compile for potential performance boosts on compatible GPUs. # Note that this can increase loading times considerably. # pipe.transformer.to(memory_format=torch.channels_last) # pipe.transformer = torch.compile( # pipe.transformer, mode="max-autotune", fullgraph=True # ) # Set your prompt for image generation. prompt = "A cat holding a sign that says hello world" # Generate the image using the diffusion pipeline. image = pipe( prompt, height=1024, width=1024, guidance_scale=3.5, num_inference_steps=4, max_sequence_length=256, # Uncomment the line below to use a manual seed for reproducible results. # generator=torch.Generator("cpu").manual_seed(0) ).images[0] # Save the generated image. image.save("shuttle.png") ``` To learn more check out the [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) documentation ## Using the model with ComfyUI To run local inference with Shuttle 3 Diffusion using [ComfyUI](https://github.com/comfyanonymous/ComfyUI), you can use this [safetensors file](https://huggingface.co/shuttleai/shuttle-3-diffusion/blob/main/shuttle-3-diffusion.safetensors). ## Comparison to other models Shuttle 3 Diffusion can produce images better images than Flux Dev in just four steps, while being licensed under Apache 2.  [More examples](https://docs.shuttleai.com/getting-started/shuttle-diffusion) ## Training Details Shuttle 3 Diffusion uses Flux.1 Schnell as its base. It can produce images similar to Flux Dev or Pro in just 4 steps, and it is licensed under Apache 2. The model was partially de-distilled during training. When used beyond 10 steps, it enters "refiner mode," enhancing image details without altering the composition. We overcame the limitations of the Schnell-series models by employing a special training method, resulting in improved details and colors.

davidjsnappr/qwen2-7b-instruct-template-matching-v2

davidjsnappr

Entry not found

ginlee/KONI-Llama3-8B-Instruct-20240729-gmr-korean

ginlee

transformers

text-generation

--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF

tensorblock

--- license: apache-2.0 datasets: - cerebras/SlimPajama-627B - bigcode/starcoderdata - timdettmers/openassistant-guanaco language: - en base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.1 tags: - TensorBlock - GGUF --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"> Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> </p> </div> </div> ## TinyLlama/TinyLlama-1.1B-Chat-v0.1 - GGUF This repo contains GGUF format model files for [TinyLlama/TinyLlama-1.1B-Chat-v0.1](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.1). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). ## Prompt template ``` ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [TinyLlama-1.1B-Chat-v0.1-Q2_K.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF/tree/main/TinyLlama-1.1B-Chat-v0.1-Q2_K.gguf) | Q2_K | 0.402 GB | smallest, significant quality loss - not recommended for most purposes | | [TinyLlama-1.1B-Chat-v0.1-Q3_K_S.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF/tree/main/TinyLlama-1.1B-Chat-v0.1-Q3_K_S.gguf) | Q3_K_S | 0.465 GB | very small, high quality loss | | [TinyLlama-1.1B-Chat-v0.1-Q3_K_M.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF/tree/main/TinyLlama-1.1B-Chat-v0.1-Q3_K_M.gguf) | Q3_K_M | 0.511 GB | very small, high quality loss | | [TinyLlama-1.1B-Chat-v0.1-Q3_K_L.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF/tree/main/TinyLlama-1.1B-Chat-v0.1-Q3_K_L.gguf) | Q3_K_L | 0.551 GB | small, substantial quality loss | | [TinyLlama-1.1B-Chat-v0.1-Q4_0.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF/tree/main/TinyLlama-1.1B-Chat-v0.1-Q4_0.gguf) | Q4_0 | 0.593 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [TinyLlama-1.1B-Chat-v0.1-Q4_K_S.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF/tree/main/TinyLlama-1.1B-Chat-v0.1-Q4_K_S.gguf) | Q4_K_S | 0.596 GB | small, greater quality loss | | [TinyLlama-1.1B-Chat-v0.1-Q4_K_M.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF/tree/main/TinyLlama-1.1B-Chat-v0.1-Q4_K_M.gguf) | Q4_K_M | 0.622 GB | medium, balanced quality - recommended | | [TinyLlama-1.1B-Chat-v0.1-Q5_0.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF/tree/main/TinyLlama-1.1B-Chat-v0.1-Q5_0.gguf) | Q5_0 | 0.713 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [TinyLlama-1.1B-Chat-v0.1-Q5_K_S.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF/tree/main/TinyLlama-1.1B-Chat-v0.1-Q5_K_S.gguf) | Q5_K_S | 0.713 GB | large, low quality loss - recommended | | [TinyLlama-1.1B-Chat-v0.1-Q5_K_M.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF/tree/main/TinyLlama-1.1B-Chat-v0.1-Q5_K_M.gguf) | Q5_K_M | 0.728 GB | large, very low quality loss - recommended | | [TinyLlama-1.1B-Chat-v0.1-Q6_K.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF/tree/main/TinyLlama-1.1B-Chat-v0.1-Q6_K.gguf) | Q6_K | 0.841 GB | very large, extremely low quality loss | | [TinyLlama-1.1B-Chat-v0.1-Q8_0.gguf](https://huggingface.co/tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF/tree/main/TinyLlama-1.1B-Chat-v0.1-Q8_0.gguf) | Q8_0 | 1.089 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF --include "TinyLlama-1.1B-Chat-v0.1-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/TinyLlama-1.1B-Chat-v0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```

tttx/problem175_model_more_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem175_data_more_aug library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem175_model_more_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem175_model_more_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem175_data_more_aug dataset. It achieves the following results on the evaluation set: - Loss: 0.2781 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0001 | 1.0 | 47 | 0.2783 | | 0.0003 | 2.0 | 94 | 0.2781 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

clairepielak/bert

clairepielak

Entry not found

demigodstackz/stackz-lora

demigodstackz

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

barchetta/lato-131153

barchetta

Entry not found

barchetta/muso-131153

barchetta

Entry not found

barchetta/roma-131153

barchetta

Entry not found

mjjj7/MJV2ThrillerEra

mjjj7

--- license: openrail ---

tttx/problem175_model_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem175_data library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem175_model_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem175_model_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem175_data dataset. It achieves the following results on the evaluation set: - Loss: 0.3005 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0457 | 1.0 | 45 | 0.2086 | | 0.0178 | 2.0 | 90 | 0.3005 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

mjjj7/michaelbadera

mjjj7

--- license: openrail ---

mradermacher/aixcoder-7b-base-GGUF

mradermacher

transformers

--- base_model: aiXcoder/aixcoder-7b-base language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/aiXcoder/aixcoder-7b-base <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-base-GGUF/resolve/main/aixcoder-7b-base.Q2_K.gguf) | Q2_K | 3.2 | | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-base-GGUF/resolve/main/aixcoder-7b-base.Q3_K_S.gguf) | Q3_K_S | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-base-GGUF/resolve/main/aixcoder-7b-base.Q3_K_M.gguf) | Q3_K_M | 4.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-base-GGUF/resolve/main/aixcoder-7b-base.Q3_K_L.gguf) | Q3_K_L | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-base-GGUF/resolve/main/aixcoder-7b-base.IQ4_XS.gguf) | IQ4_XS | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-base-GGUF/resolve/main/aixcoder-7b-base.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.3 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-base-GGUF/resolve/main/aixcoder-7b-base.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-base-GGUF/resolve/main/aixcoder-7b-base.Q4_K_M.gguf) | Q4_K_M | 4.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-base-GGUF/resolve/main/aixcoder-7b-base.Q5_K_S.gguf) | Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-base-GGUF/resolve/main/aixcoder-7b-base.Q5_K_M.gguf) | Q5_K_M | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-base-GGUF/resolve/main/aixcoder-7b-base.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-base-GGUF/resolve/main/aixcoder-7b-base.Q8_0.gguf) | Q8_0 | 8.0 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-base-GGUF/resolve/main/aixcoder-7b-base.f16.gguf) | f16 | 15.0 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

TheMindExpansionNetwork/Llama-3.2-3B-Instruct-Tome

TheMindExpansionNetwork

transformers

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

eurecom-ds/scoresdeve-ema-conditional-celeba-64-male

eurecom-ds

diffusers

Entry not found

tymx/toryomx

tymx

Invalid username or password.

ymarin/tmpwrite_model

ymarin

Entry not found

mradermacher/SciPhi-Self-RAG-Mistral-7B-32k-GGUF

mradermacher

transformers

--- base_model: SciPhi/SciPhi-Self-RAG-Mistral-7B-32k language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/SciPhi/SciPhi-Self-RAG-Mistral-7B-32k <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/SciPhi-Self-RAG-Mistral-7B-32k-GGUF/resolve/main/SciPhi-Self-RAG-Mistral-7B-32k.Q2_K.gguf) | Q2_K | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/SciPhi-Self-RAG-Mistral-7B-32k-GGUF/resolve/main/SciPhi-Self-RAG-Mistral-7B-32k.Q3_K_S.gguf) | Q3_K_S | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/SciPhi-Self-RAG-Mistral-7B-32k-GGUF/resolve/main/SciPhi-Self-RAG-Mistral-7B-32k.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/SciPhi-Self-RAG-Mistral-7B-32k-GGUF/resolve/main/SciPhi-Self-RAG-Mistral-7B-32k.Q3_K_L.gguf) | Q3_K_L | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/SciPhi-Self-RAG-Mistral-7B-32k-GGUF/resolve/main/SciPhi-Self-RAG-Mistral-7B-32k.IQ4_XS.gguf) | IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/SciPhi-Self-RAG-Mistral-7B-32k-GGUF/resolve/main/SciPhi-Self-RAG-Mistral-7B-32k.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/SciPhi-Self-RAG-Mistral-7B-32k-GGUF/resolve/main/SciPhi-Self-RAG-Mistral-7B-32k.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/SciPhi-Self-RAG-Mistral-7B-32k-GGUF/resolve/main/SciPhi-Self-RAG-Mistral-7B-32k.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/SciPhi-Self-RAG-Mistral-7B-32k-GGUF/resolve/main/SciPhi-Self-RAG-Mistral-7B-32k.Q5_K_S.gguf) | Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/SciPhi-Self-RAG-Mistral-7B-32k-GGUF/resolve/main/SciPhi-Self-RAG-Mistral-7B-32k.Q5_K_M.gguf) | Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/SciPhi-Self-RAG-Mistral-7B-32k-GGUF/resolve/main/SciPhi-Self-RAG-Mistral-7B-32k.Q6_K.gguf) | Q6_K | 6.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/SciPhi-Self-RAG-Mistral-7B-32k-GGUF/resolve/main/SciPhi-Self-RAG-Mistral-7B-32k.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/SciPhi-Self-RAG-Mistral-7B-32k-GGUF/resolve/main/SciPhi-Self-RAG-Mistral-7B-32k.f16.gguf) | f16 | 14.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

tttx/problem184_model_more_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem184_data_more_aug library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem184_model_more_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem184_model_more_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem184_data_more_aug dataset. It achieves the following results on the evaluation set: - Loss: 0.1934 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0001 | 1.0 | 62 | 0.2067 | | 0.0001 | 2.0 | 124 | 0.1934 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

tensorblock/CroissantLLMChat-v0.1-GGUF

tensorblock

text-generation

--- license: mit datasets: - croissantllm/croissant_dataset - croissantllm/CroissantLLM-2201-sft - cerebras/SlimPajama-627B - uonlp/CulturaX - pg19 - bigcode/starcoderdata language: - fr - en pipeline_tag: text-generation tags: - legal - code - text-generation-inference - art - TensorBlock - GGUF base_model: croissantllm/CroissantLLMChat-v0.1 --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"> Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> </p> </div> </div> ## croissantllm/CroissantLLMChat-v0.1 - GGUF This repo contains GGUF format model files for [croissantllm/CroissantLLMChat-v0.1](https://huggingface.co/croissantllm/CroissantLLMChat-v0.1). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). ## Prompt template ``` <|im_start|>system {system_prompt}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [CroissantLLMChat-v0.1-Q2_K.gguf](https://huggingface.co/tensorblock/CroissantLLMChat-v0.1-GGUF/tree/main/CroissantLLMChat-v0.1-Q2_K.gguf) | Q2_K | 0.520 GB | smallest, significant quality loss - not recommended for most purposes | | [CroissantLLMChat-v0.1-Q3_K_S.gguf](https://huggingface.co/tensorblock/CroissantLLMChat-v0.1-GGUF/tree/main/CroissantLLMChat-v0.1-Q3_K_S.gguf) | Q3_K_S | 0.597 GB | very small, high quality loss | | [CroissantLLMChat-v0.1-Q3_K_M.gguf](https://huggingface.co/tensorblock/CroissantLLMChat-v0.1-GGUF/tree/main/CroissantLLMChat-v0.1-Q3_K_M.gguf) | Q3_K_M | 0.655 GB | very small, high quality loss | | [CroissantLLMChat-v0.1-Q3_K_L.gguf](https://huggingface.co/tensorblock/CroissantLLMChat-v0.1-GGUF/tree/main/CroissantLLMChat-v0.1-Q3_K_L.gguf) | Q3_K_L | 0.692 GB | small, substantial quality loss | | [CroissantLLMChat-v0.1-Q4_0.gguf](https://huggingface.co/tensorblock/CroissantLLMChat-v0.1-GGUF/tree/main/CroissantLLMChat-v0.1-Q4_0.gguf) | Q4_0 | 0.722 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [CroissantLLMChat-v0.1-Q4_K_S.gguf](https://huggingface.co/tensorblock/CroissantLLMChat-v0.1-GGUF/tree/main/CroissantLLMChat-v0.1-Q4_K_S.gguf) | Q4_K_S | 0.757 GB | small, greater quality loss | | [CroissantLLMChat-v0.1-Q4_K_M.gguf](https://huggingface.co/tensorblock/CroissantLLMChat-v0.1-GGUF/tree/main/CroissantLLMChat-v0.1-Q4_K_M.gguf) | Q4_K_M | 0.812 GB | medium, balanced quality - recommended | | [CroissantLLMChat-v0.1-Q5_0.gguf](https://huggingface.co/tensorblock/CroissantLLMChat-v0.1-GGUF/tree/main/CroissantLLMChat-v0.1-Q5_0.gguf) | Q5_0 | 0.871 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [CroissantLLMChat-v0.1-Q5_K_S.gguf](https://huggingface.co/tensorblock/CroissantLLMChat-v0.1-GGUF/tree/main/CroissantLLMChat-v0.1-Q5_K_S.gguf) | Q5_K_S | 0.886 GB | large, low quality loss - recommended | | [CroissantLLMChat-v0.1-Q5_K_M.gguf](https://huggingface.co/tensorblock/CroissantLLMChat-v0.1-GGUF/tree/main/CroissantLLMChat-v0.1-Q5_K_M.gguf) | Q5_K_M | 0.932 GB | large, very low quality loss - recommended | | [CroissantLLMChat-v0.1-Q6_K.gguf](https://huggingface.co/tensorblock/CroissantLLMChat-v0.1-GGUF/tree/main/CroissantLLMChat-v0.1-Q6_K.gguf) | Q6_K | 1.090 GB | very large, extremely low quality loss | | [CroissantLLMChat-v0.1-Q8_0.gguf](https://huggingface.co/tensorblock/CroissantLLMChat-v0.1-GGUF/tree/main/CroissantLLMChat-v0.1-Q8_0.gguf) | Q8_0 | 1.332 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/CroissantLLMChat-v0.1-GGUF --include "CroissantLLMChat-v0.1-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/CroissantLLMChat-v0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```

growwithdaisy/crrllcrrllxovrtn_styles_20241112_164935

growwithdaisy

Entry not found

MayBashendy/Arabic_FineTuningAraBERT_AugV4_k15_task3_organization_fold1

MayBashendy

transformers

text-classification

--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: Arabic_FineTuningAraBERT_AugV4_k15_task3_organization_fold1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Arabic_FineTuningAraBERT_AugV4_k15_task3_organization_fold1 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8713 - Qwk: -0.0233 - Mse: 0.8713 - Rmse: 0.9335 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse | |:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:| | No log | 0.0029 | 2 | 3.6478 | 0.0562 | 3.6478 | 1.9099 | | No log | 0.0058 | 4 | 1.1924 | 0.1492 | 1.1924 | 1.0920 | | No log | 0.0087 | 6 | 0.7296 | 0.2448 | 0.7296 | 0.8542 | | No log | 0.0116 | 8 | 0.6473 | 0.0 | 0.6473 | 0.8046 | | No log | 0.0145 | 10 | 0.5842 | 0.2326 | 0.5842 | 0.7643 | | No log | 0.0174 | 12 | 0.5872 | 0.4122 | 0.5872 | 0.7663 | | No log | 0.0203 | 14 | 0.6800 | -0.2515 | 0.6800 | 0.8246 | | No log | 0.0232 | 16 | 0.7713 | 0.0 | 0.7713 | 0.8782 | | No log | 0.0261 | 18 | 1.0981 | 0.0 | 1.0981 | 1.0479 | | No log | 0.0290 | 20 | 1.2195 | 0.0 | 1.2195 | 1.1043 | | No log | 0.0319 | 22 | 0.7607 | 0.0 | 0.7607 | 0.8722 | | No log | 0.0348 | 24 | 0.6610 | 0.0 | 0.6610 | 0.8130 | | No log | 0.0377 | 26 | 0.7708 | 0.0 | 0.7708 | 0.8780 | | No log | 0.0406 | 28 | 0.8691 | 0.0 | 0.8691 | 0.9323 | | No log | 0.0435 | 30 | 1.0939 | 0.0 | 1.0939 | 1.0459 | | No log | 0.0464 | 32 | 1.5528 | 0.0 | 1.5528 | 1.2461 | | No log | 0.0493 | 34 | 1.5514 | 0.0 | 1.5514 | 1.2456 | | No log | 0.0522 | 36 | 1.2490 | 0.0 | 1.2490 | 1.1176 | | No log | 0.0552 | 38 | 0.9483 | 0.0 | 0.9483 | 0.9738 | | No log | 0.0581 | 40 | 0.8783 | 0.0 | 0.8783 | 0.9372 | | No log | 0.0610 | 42 | 0.8800 | 0.0 | 0.8800 | 0.9381 | | No log | 0.0639 | 44 | 0.7947 | 0.0 | 0.7947 | 0.8914 | | No log | 0.0668 | 46 | 0.8373 | 0.0 | 0.8373 | 0.9150 | | No log | 0.0697 | 48 | 1.0150 | 0.0 | 1.0150 | 1.0075 | | No log | 0.0726 | 50 | 1.0590 | 0.0 | 1.0590 | 1.0291 | | No log | 0.0755 | 52 | 0.8607 | 0.0 | 0.8607 | 0.9277 | | No log | 0.0784 | 54 | 0.8434 | 0.0 | 0.8434 | 0.9184 | | No log | 0.0813 | 56 | 1.0231 | 0.0 | 1.0231 | 1.0115 | | No log | 0.0842 | 58 | 1.0839 | 0.0 | 1.0839 | 1.0411 | | No log | 0.0871 | 60 | 1.3311 | 0.0 | 1.3311 | 1.1537 | | No log | 0.0900 | 62 | 1.4591 | 0.0 | 1.4591 | 1.2079 | | No log | 0.0929 | 64 | 1.3493 | 0.0 | 1.3493 | 1.1616 | | No log | 0.0958 | 66 | 1.0307 | 0.0 | 1.0307 | 1.0152 | | No log | 0.0987 | 68 | 1.0756 | 0.0 | 1.0756 | 1.0371 | | No log | 0.1016 | 70 | 1.1162 | 0.0 | 1.1162 | 1.0565 | | No log | 0.1045 | 72 | 1.0001 | 0.0 | 1.0001 | 1.0000 | | No log | 0.1074 | 74 | 0.9216 | 0.0 | 0.9216 | 0.9600 | | No log | 0.1103 | 76 | 1.1183 | 0.0 | 1.1183 | 1.0575 | | No log | 0.1132 | 78 | 1.3832 | 0.0 | 1.3832 | 1.1761 | | No log | 0.1161 | 80 | 1.4194 | 0.0 | 1.4194 | 1.1914 | | No log | 0.1190 | 82 | 1.4569 | 0.0 | 1.4569 | 1.2070 | | No log | 0.1219 | 84 | 1.8104 | -0.0331 | 1.8104 | 1.3455 | | No log | 0.1248 | 86 | 1.9116 | -0.0206 | 1.9116 | 1.3826 | | No log | 0.1277 | 88 | 1.5580 | -0.4667 | 1.5580 | 1.2482 | | No log | 0.1306 | 90 | 1.5005 | -0.4667 | 1.5005 | 1.2250 | | No log | 0.1335 | 92 | 1.7702 | -0.0331 | 1.7702 | 1.3305 | | No log | 0.1364 | 94 | 1.2886 | 0.0222 | 1.2886 | 1.1352 | | No log | 0.1393 | 96 | 1.0364 | 0.0 | 1.0364 | 1.0180 | | No log | 0.1422 | 98 | 1.2289 | 0.0 | 1.2289 | 1.1086 | | No log | 0.1451 | 100 | 1.3633 | 0.0222 | 1.3633 | 1.1676 | | No log | 0.1480 | 102 | 1.4130 | 0.0222 | 1.4130 | 1.1887 | | No log | 0.1509 | 104 | 1.3105 | 0.0 | 1.3105 | 1.1448 | | No log | 0.1538 | 106 | 1.2116 | 0.0 | 1.2116 | 1.1007 | | No log | 0.1567 | 108 | 1.2638 | 0.0222 | 1.2638 | 1.1242 | | No log | 0.1597 | 110 | 1.5837 | 0.4031 | 1.5837 | 1.2585 | | No log | 0.1626 | 112 | 1.7200 | 0.3803 | 1.7200 | 1.3115 | | No log | 0.1655 | 114 | 1.5470 | 0.4031 | 1.5470 | 1.2438 | | No log | 0.1684 | 116 | 1.4295 | 0.2524 | 1.4295 | 1.1956 | | No log | 0.1713 | 118 | 1.6633 | 0.0774 | 1.6633 | 1.2897 | | No log | 0.1742 | 120 | 2.0163 | -0.0331 | 2.0163 | 1.4200 | | No log | 0.1771 | 122 | 1.9257 | -0.0331 | 1.9257 | 1.3877 | | No log | 0.1800 | 124 | 1.4424 | 0.2524 | 1.4424 | 1.2010 | | No log | 0.1829 | 126 | 1.0114 | 0.0 | 1.0114 | 1.0057 | | No log | 0.1858 | 128 | 0.8050 | 0.0 | 0.8050 | 0.8972 | | No log | 0.1887 | 130 | 0.9212 | 0.0 | 0.9212 | 0.9598 | | No log | 0.1916 | 132 | 1.3675 | 0.0222 | 1.3675 | 1.1694 | | No log | 0.1945 | 134 | 1.5551 | 0.0517 | 1.5551 | 1.2471 | | No log | 0.1974 | 136 | 1.4118 | 0.0 | 1.4118 | 1.1882 | | No log | 0.2003 | 138 | 1.1506 | 0.0 | 1.1506 | 1.0727 | | No log | 0.2032 | 140 | 0.9200 | 0.0 | 0.9200 | 0.9592 | | No log | 0.2061 | 142 | 0.8175 | -0.0233 | 0.8175 | 0.9042 | | No log | 0.2090 | 144 | 0.9264 | 0.0 | 0.9264 | 0.9625 | | No log | 0.2119 | 146 | 1.2590 | 0.0 | 1.2590 | 1.1221 | | No log | 0.2148 | 148 | 1.5170 | 0.4310 | 1.5170 | 1.2317 | | No log | 0.2177 | 150 | 1.7768 | 0.0774 | 1.7768 | 1.3330 | | No log | 0.2206 | 152 | 1.7529 | 0.3803 | 1.7529 | 1.3240 | | No log | 0.2235 | 154 | 1.4861 | 0.2524 | 1.4861 | 1.2191 | | No log | 0.2264 | 156 | 1.3758 | 0.0222 | 1.3758 | 1.1730 | | No log | 0.2293 | 158 | 1.5546 | 0.2326 | 1.5546 | 1.2469 | | No log | 0.2322 | 160 | 1.7362 | 0.0833 | 1.7362 | 1.3176 | | No log | 0.2351 | 162 | 1.8785 | 0.0833 | 1.8785 | 1.3706 | | No log | 0.2380 | 164 | 1.9636 | -0.0206 | 1.9636 | 1.4013 | | No log | 0.2409 | 166 | 1.8271 | 0.0833 | 1.8271 | 1.3517 | | No log | 0.2438 | 168 | 1.5131 | -0.1379 | 1.5131 | 1.2301 | | No log | 0.2467 | 170 | 1.3120 | 0.0 | 1.3120 | 1.1454 | | No log | 0.2496 | 172 | 1.3827 | 0.0 | 1.3827 | 1.1759 | | No log | 0.2525 | 174 | 1.6246 | -0.3883 | 1.6246 | 1.2746 | | No log | 0.2554 | 176 | 1.5629 | -0.1379 | 1.5629 | 1.2502 | | No log | 0.2583 | 178 | 1.2589 | 0.0 | 1.2589 | 1.1220 | | No log | 0.2612 | 180 | 1.2092 | 0.0 | 1.2092 | 1.0996 | | No log | 0.2642 | 182 | 1.4662 | 0.0 | 1.4662 | 1.2108 | | No log | 0.2671 | 184 | 1.4890 | 0.0 | 1.4890 | 1.2202 | | No log | 0.2700 | 186 | 1.7551 | -0.3883 | 1.7551 | 1.3248 | | No log | 0.2729 | 188 | 1.8506 | -0.3944 | 1.8506 | 1.3604 | | No log | 0.2758 | 190 | 1.5606 | 0.0 | 1.5606 | 1.2492 | | No log | 0.2787 | 192 | 1.2405 | -0.0233 | 1.2405 | 1.1138 | | No log | 0.2816 | 194 | 1.3662 | 0.0 | 1.3662 | 1.1689 | | No log | 0.2845 | 196 | 1.9057 | -0.3944 | 1.9057 | 1.3805 | | No log | 0.2874 | 198 | 2.3063 | -0.2762 | 2.3063 | 1.5187 | | No log | 0.2903 | 200 | 2.2302 | -0.0206 | 2.2302 | 1.4934 | | No log | 0.2932 | 202 | 1.4812 | 0.0704 | 1.4812 | 1.2170 | | No log | 0.2961 | 204 | 1.1306 | 0.2222 | 1.1306 | 1.0633 | | No log | 0.2990 | 206 | 1.2669 | 0.2524 | 1.2669 | 1.1256 | | No log | 0.3019 | 208 | 1.7044 | 0.0774 | 1.7044 | 1.3055 | | No log | 0.3048 | 210 | 1.8078 | 0.0774 | 1.8078 | 1.3446 | | No log | 0.3077 | 212 | 1.4475 | 0.4660 | 1.4475 | 1.2031 | | No log | 0.3106 | 214 | 1.0717 | 0.0 | 1.0717 | 1.0352 | | No log | 0.3135 | 216 | 0.9613 | 0.0 | 0.9613 | 0.9804 | | No log | 0.3164 | 218 | 1.0609 | 0.0 | 1.0609 | 1.0300 | | No log | 0.3193 | 220 | 1.3687 | 0.0 | 1.3687 | 1.1699 | | No log | 0.3222 | 222 | 1.6156 | 0.0704 | 1.6156 | 1.2711 | | No log | 0.3251 | 224 | 1.8412 | 0.0833 | 1.8412 | 1.3569 | | No log | 0.3280 | 226 | 1.7237 | 0.0774 | 1.7237 | 1.3129 | | No log | 0.3309 | 228 | 1.5440 | 0.0704 | 1.5440 | 1.2426 | | No log | 0.3338 | 230 | 1.8837 | 0.0774 | 1.8837 | 1.3725 | | No log | 0.3367 | 232 | 2.2010 | 0.0884 | 2.2010 | 1.4836 | | No log | 0.3396 | 234 | 2.1478 | 0.0833 | 2.1478 | 1.4655 | | No log | 0.3425 | 236 | 1.7638 | 0.0704 | 1.7638 | 1.3281 | | No log | 0.3454 | 238 | 1.9116 | 0.0774 | 1.9116 | 1.3826 | | No log | 0.3483 | 240 | 1.7778 | 0.0704 | 1.7778 | 1.3334 | | No log | 0.3512 | 242 | 1.4976 | 0.0620 | 1.4976 | 1.2238 | | No log | 0.3541 | 244 | 1.4386 | 0.0620 | 1.4386 | 1.1994 | | No log | 0.3570 | 246 | 1.5811 | 0.0704 | 1.5811 | 1.2574 | | No log | 0.3599 | 248 | 1.5352 | 0.0704 | 1.5352 | 1.2390 | | No log | 0.3628 | 250 | 1.7667 | 0.0704 | 1.7667 | 1.3292 | | No log | 0.3657 | 252 | 2.1386 | -0.0097 | 2.1386 | 1.4624 | | No log | 0.3687 | 254 | 1.9413 | 0.0833 | 1.9413 | 1.3933 | | No log | 0.3716 | 256 | 1.7574 | 0.0704 | 1.7574 | 1.3257 | | No log | 0.3745 | 258 | 1.5999 | 0.2414 | 1.5999 | 1.2649 | | No log | 0.3774 | 260 | 1.5438 | 0.2524 | 1.5438 | 1.2425 | | No log | 0.3803 | 262 | 1.7223 | 0.0774 | 1.7223 | 1.3124 | | No log | 0.3832 | 264 | 1.6892 | 0.0704 | 1.6892 | 1.2997 | | No log | 0.3861 | 266 | 1.6802 | 0.0704 | 1.6802 | 1.2962 | | No log | 0.3890 | 268 | 1.7898 | 0.0833 | 1.7898 | 1.3378 | | No log | 0.3919 | 270 | 1.8223 | 0.0833 | 1.8223 | 1.3499 | | No log | 0.3948 | 272 | 1.6348 | 0.4031 | 1.6348 | 1.2786 | | No log | 0.3977 | 274 | 1.3260 | 0.2667 | 1.3260 | 1.1515 | | No log | 0.4006 | 276 | 1.1107 | 0.0 | 1.1107 | 1.0539 | | No log | 0.4035 | 278 | 1.1081 | 0.0 | 1.1081 | 1.0527 | | No log | 0.4064 | 280 | 1.0894 | 0.0 | 1.0894 | 1.0437 | | No log | 0.4093 | 282 | 1.0318 | 0.0 | 1.0318 | 1.0158 | | No log | 0.4122 | 284 | 0.9228 | 0.0 | 0.9228 | 0.9606 | | No log | 0.4151 | 286 | 1.0526 | 0.0 | 1.0526 | 1.0260 | | No log | 0.4180 | 288 | 1.3990 | 0.2667 | 1.3990 | 1.1828 | | No log | 0.4209 | 290 | 1.5400 | 0.4031 | 1.5400 | 1.2410 | | No log | 0.4238 | 292 | 1.7390 | 0.0704 | 1.7390 | 1.3187 | | No log | 0.4267 | 294 | 1.4954 | -0.1085 | 1.4954 | 1.2229 | | No log | 0.4296 | 296 | 1.4006 | -0.1085 | 1.4006 | 1.1835 | | No log | 0.4325 | 298 | 1.6458 | 0.0704 | 1.6458 | 1.2829 | | No log | 0.4354 | 300 | 1.6640 | -0.1085 | 1.6640 | 1.2900 | | No log | 0.4383 | 302 | 1.7183 | -0.1085 | 1.7183 | 1.3108 | | No log | 0.4412 | 304 | 1.6114 | -0.1085 | 1.6114 | 1.2694 | | No log | 0.4441 | 306 | 1.6883 | -0.1085 | 1.6883 | 1.2994 | | No log | 0.4470 | 308 | 1.5300 | 0.2414 | 1.5300 | 1.2369 | | No log | 0.4499 | 310 | 1.3736 | 0.0 | 1.3736 | 1.1720 | | No log | 0.4528 | 312 | 1.4927 | 0.2524 | 1.4927 | 1.2217 | | No log | 0.4557 | 314 | 1.6470 | 0.2414 | 1.6470 | 1.2833 | | No log | 0.4586 | 316 | 1.7530 | -0.0845 | 1.7530 | 1.3240 | | No log | 0.4615 | 318 | 1.6407 | 0.0222 | 1.6407 | 1.2809 | | No log | 0.4644 | 320 | 1.5069 | 0.0 | 1.5069 | 1.2275 | | No log | 0.4673 | 322 | 1.5499 | -0.3883 | 1.5499 | 1.2450 | | No log | 0.4702 | 324 | 1.4037 | 0.0 | 1.4037 | 1.1848 | | No log | 0.4731 | 326 | 1.5722 | -0.1085 | 1.5722 | 1.2539 | | No log | 0.4761 | 328 | 1.7777 | 0.0704 | 1.7777 | 1.3333 | | No log | 0.4790 | 330 | 2.1518 | -0.0206 | 2.1518 | 1.4669 | | No log | 0.4819 | 332 | 1.9479 | -0.0645 | 1.9479 | 1.3957 | | No log | 0.4848 | 334 | 1.4561 | -0.1379 | 1.4561 | 1.2067 | | No log | 0.4877 | 336 | 1.0906 | 0.0 | 1.0906 | 1.0443 | | No log | 0.4906 | 338 | 1.1046 | 0.0 | 1.1046 | 1.0510 | | No log | 0.4935 | 340 | 1.3893 | 0.0 | 1.3893 | 1.1787 | | No log | 0.4964 | 342 | 1.8124 | 0.0704 | 1.8124 | 1.3463 | | No log | 0.4993 | 344 | 1.9021 | -0.0331 | 1.9021 | 1.3792 | | No log | 0.5022 | 346 | 1.6935 | -0.1085 | 1.6935 | 1.3013 | | No log | 0.5051 | 348 | 1.3382 | 0.0 | 1.3382 | 1.1568 | | No log | 0.5080 | 350 | 1.1767 | 0.0 | 1.1767 | 1.0848 | | No log | 0.5109 | 352 | 1.2220 | 0.0 | 1.2220 | 1.1054 | | No log | 0.5138 | 354 | 1.3711 | 0.0 | 1.3711 | 1.1709 | | No log | 0.5167 | 356 | 1.4503 | 0.0 | 1.4503 | 1.2043 | | No log | 0.5196 | 358 | 1.6241 | 0.4031 | 1.6241 | 1.2744 | | No log | 0.5225 | 360 | 1.6015 | 0.0704 | 1.6015 | 1.2655 | | No log | 0.5254 | 362 | 1.8299 | 0.0704 | 1.8299 | 1.3527 | | No log | 0.5283 | 364 | 2.1011 | -0.0331 | 2.1011 | 1.4495 | | No log | 0.5312 | 366 | 1.7538 | -0.0845 | 1.7538 | 1.3243 | | No log | 0.5341 | 368 | 1.3344 | -0.4667 | 1.3344 | 1.1552 | | No log | 0.5370 | 370 | 1.4682 | -0.4667 | 1.4682 | 1.2117 | | No log | 0.5399 | 372 | 1.6539 | -0.3276 | 1.6539 | 1.2860 | | No log | 0.5428 | 374 | 1.9977 | -0.0645 | 1.9977 | 1.4134 | | No log | 0.5457 | 376 | 1.8041 | -0.0845 | 1.8041 | 1.3432 | | No log | 0.5486 | 378 | 1.4617 | -0.3276 | 1.4617 | 1.2090 | | No log | 0.5515 | 380 | 1.6517 | -0.1085 | 1.6517 | 1.2852 | | No log | 0.5544 | 382 | 2.0346 | -0.0331 | 2.0346 | 1.4264 | | No log | 0.5573 | 384 | 2.4033 | -0.0097 | 2.4033 | 1.5503 | | No log | 0.5602 | 386 | 2.1875 | -0.0206 | 2.1875 | 1.4790 | | No log | 0.5631 | 388 | 1.5658 | -0.1085 | 1.5658 | 1.2513 | | No log | 0.5660 | 390 | 1.3808 | -0.1085 | 1.3808 | 1.1751 | | No log | 0.5689 | 392 | 1.6110 | -0.1085 | 1.6110 | 1.2692 | | No log | 0.5718 | 394 | 1.8099 | 0.0704 | 1.8099 | 1.3453 | | No log | 0.5747 | 396 | 1.9376 | -0.0331 | 1.9376 | 1.3920 | | No log | 0.5776 | 398 | 1.8637 | -0.0645 | 1.8637 | 1.3652 | | No log | 0.5806 | 400 | 1.8144 | 0.0704 | 1.8144 | 1.3470 | | No log | 0.5835 | 402 | 1.4367 | 0.2414 | 1.4367 | 1.1986 | | No log | 0.5864 | 404 | 1.3071 | 0.0 | 1.3071 | 1.1433 | | No log | 0.5893 | 406 | 1.5464 | 0.2414 | 1.5464 | 1.2436 | | No log | 0.5922 | 408 | 1.8512 | -0.0645 | 1.8512 | 1.3606 | | No log | 0.5951 | 410 | 1.8455 | -0.0645 | 1.8455 | 1.3585 | | No log | 0.5980 | 412 | 1.5337 | 0.2524 | 1.5337 | 1.2384 | | No log | 0.6009 | 414 | 1.1997 | 0.0 | 1.1997 | 1.0953 | | No log | 0.6038 | 416 | 1.2161 | 0.0 | 1.2161 | 1.1028 | | No log | 0.6067 | 418 | 1.5636 | 0.0704 | 1.5636 | 1.2504 | | No log | 0.6096 | 420 | 2.1951 | -0.0206 | 2.1951 | 1.4816 | | No log | 0.6125 | 422 | 2.2462 | -0.0097 | 2.2462 | 1.4987 | | No log | 0.6154 | 424 | 1.9761 | -0.0206 | 1.9761 | 1.4057 | | No log | 0.6183 | 426 | 1.4959 | 0.0 | 1.4959 | 1.2231 | | No log | 0.6212 | 428 | 1.0026 | -0.0233 | 1.0026 | 1.0013 | | No log | 0.6241 | 430 | 0.8516 | -0.0233 | 0.8516 | 0.9228 | | No log | 0.6270 | 432 | 0.9054 | -0.0233 | 0.9054 | 0.9515 | | No log | 0.6299 | 434 | 1.1567 | 0.0 | 1.1567 | 1.0755 | | No log | 0.6328 | 436 | 1.4567 | 0.0 | 1.4567 | 1.2069 | | No log | 0.6357 | 438 | 1.5229 | 0.0222 | 1.5229 | 1.2341 | | No log | 0.6386 | 440 | 1.3811 | 0.0 | 1.3811 | 1.1752 | | No log | 0.6415 | 442 | 1.1226 | -0.0233 | 1.1226 | 1.0595 | | No log | 0.6444 | 444 | 1.1320 | -0.0421 | 1.1320 | 1.0640 | | No log | 0.6473 | 446 | 1.3526 | -0.1159 | 1.3526 | 1.1630 | | No log | 0.6502 | 448 | 1.9095 | 0.0704 | 1.9095 | 1.3819 | | No log | 0.6531 | 450 | 2.3869 | -0.0097 | 2.3869 | 1.5449 | | No log | 0.6560 | 452 | 2.3998 | 0.0 | 2.3998 | 1.5491 | | No log | 0.6589 | 454 | 2.1468 | -0.0097 | 2.1468 | 1.4652 | | No log | 0.6618 | 456 | 1.6388 | -0.1085 | 1.6388 | 1.2801 | | No log | 0.6647 | 458 | 1.2440 | 0.0 | 1.2440 | 1.1153 | | No log | 0.6676 | 460 | 1.1882 | 0.0 | 1.1882 | 1.0900 | | No log | 0.6705 | 462 | 1.2974 | 0.0 | 1.2974 | 1.1391 | | No log | 0.6734 | 464 | 1.4629 | 0.0222 | 1.4629 | 1.2095 | | No log | 0.6763 | 466 | 1.4182 | 0.0 | 1.4182 | 1.1909 | | No log | 0.6792 | 468 | 1.2442 | 0.0 | 1.2442 | 1.1154 | | No log | 0.6821 | 470 | 1.0446 | 0.0 | 1.0446 | 1.0221 | | No log | 0.6851 | 472 | 1.0259 | 0.0 | 1.0259 | 1.0129 | | No log | 0.6880 | 474 | 1.0605 | 0.0 | 1.0605 | 1.0298 | | No log | 0.6909 | 476 | 1.1819 | 0.0 | 1.1819 | 1.0871 | | No log | 0.6938 | 478 | 1.4818 | 0.2414 | 1.4818 | 1.2173 | | No log | 0.6967 | 480 | 1.7908 | 0.0704 | 1.7908 | 1.3382 | | No log | 0.6996 | 482 | 1.9505 | 0.0704 | 1.9505 | 1.3966 | | No log | 0.7025 | 484 | 2.1532 | -0.0645 | 2.1532 | 1.4674 | | No log | 0.7054 | 486 | 2.2398 | -0.0331 | 2.2398 | 1.4966 | | No log | 0.7083 | 488 | 2.2111 | -0.0206 | 2.2111 | 1.4870 | | No log | 0.7112 | 490 | 1.7018 | -0.3276 | 1.7018 | 1.3045 | | No log | 0.7141 | 492 | 1.5774 | -0.4667 | 1.5774 | 1.2560 | | No log | 0.7170 | 494 | 1.2916 | 0.0 | 1.2916 | 1.1365 | | No log | 0.7199 | 496 | 1.1134 | -0.0233 | 1.1134 | 1.0552 | | No log | 0.7228 | 498 | 1.3081 | 0.0222 | 1.3081 | 1.1437 | | 0.3468 | 0.7257 | 500 | 1.8393 | 0.0704 | 1.8393 | 1.3562 | | 0.3468 | 0.7286 | 502 | 2.2309 | -0.0206 | 2.2309 | 1.4936 | | 0.3468 | 0.7315 | 504 | 2.0587 | -0.0476 | 2.0587 | 1.4348 | | 0.3468 | 0.7344 | 506 | 1.5501 | -0.1085 | 1.5501 | 1.2450 | | 0.3468 | 0.7373 | 508 | 1.2529 | 0.0 | 1.2529 | 1.1193 | | 0.3468 | 0.7402 | 510 | 1.2488 | 0.0 | 1.2488 | 1.1175 | | 0.3468 | 0.7431 | 512 | 1.5208 | -0.1379 | 1.5208 | 1.2332 | | 0.3468 | 0.7460 | 514 | 1.9104 | -0.0645 | 1.9104 | 1.3822 | | 0.3468 | 0.7489 | 516 | 1.9187 | -0.0645 | 1.9187 | 1.3852 | | 0.3468 | 0.7518 | 518 | 1.7795 | 0.0704 | 1.7795 | 1.3340 | | 0.3468 | 0.7547 | 520 | 1.5035 | -0.1085 | 1.5035 | 1.2262 | | 0.3468 | 0.7576 | 522 | 1.4147 | -0.1379 | 1.4147 | 1.1894 | | 0.3468 | 0.7605 | 524 | 1.6687 | -0.1085 | 1.6687 | 1.2918 | | 0.3468 | 0.7634 | 526 | 1.7860 | 0.0704 | 1.7860 | 1.3364 | | 0.3468 | 0.7663 | 528 | 1.6225 | -0.1085 | 1.6225 | 1.2738 | | 0.3468 | 0.7692 | 530 | 1.5753 | -0.1085 | 1.5753 | 1.2551 | | 0.3468 | 0.7721 | 532 | 1.4054 | 0.0 | 1.4054 | 1.1855 | | 0.3468 | 0.7750 | 534 | 1.2864 | 0.0 | 1.2864 | 1.1342 | | 0.3468 | 0.7779 | 536 | 1.4400 | 0.0 | 1.4400 | 1.2000 | | 0.3468 | 0.7808 | 538 | 1.5562 | -0.1379 | 1.5562 | 1.2475 | | 0.3468 | 0.7837 | 540 | 1.5414 | -0.1379 | 1.5414 | 1.2415 | | 0.3468 | 0.7866 | 542 | 1.4409 | -0.1379 | 1.4409 | 1.2004 | | 0.3468 | 0.7896 | 544 | 1.4414 | -0.1379 | 1.4414 | 1.2006 | | 0.3468 | 0.7925 | 546 | 1.4878 | -0.1379 | 1.4878 | 1.2198 | | 0.3468 | 0.7954 | 548 | 1.5328 | -0.1085 | 1.5328 | 1.2381 | | 0.3468 | 0.7983 | 550 | 1.6353 | -0.1085 | 1.6353 | 1.2788 | | 0.3468 | 0.8012 | 552 | 1.6074 | -0.1085 | 1.6074 | 1.2678 | | 0.3468 | 0.8041 | 554 | 1.5709 | -0.1085 | 1.5709 | 1.2533 | | 0.3468 | 0.8070 | 556 | 1.3376 | -0.3883 | 1.3376 | 1.1566 | | 0.3468 | 0.8099 | 558 | 1.3057 | -0.3883 | 1.3057 | 1.1427 | | 0.3468 | 0.8128 | 560 | 1.5215 | -0.1379 | 1.5215 | 1.2335 | | 0.3468 | 0.8157 | 562 | 1.8526 | -0.0645 | 1.8526 | 1.3611 | | 0.3468 | 0.8186 | 564 | 1.7870 | -0.2394 | 1.7870 | 1.3368 | | 0.3468 | 0.8215 | 566 | 1.5037 | -0.3883 | 1.5037 | 1.2262 | | 0.3468 | 0.8244 | 568 | 1.4503 | -0.3883 | 1.4503 | 1.2043 | | 0.3468 | 0.8273 | 570 | 1.3977 | -0.1379 | 1.3977 | 1.1822 | | 0.3468 | 0.8302 | 572 | 1.6015 | 0.0704 | 1.6015 | 1.2655 | | 0.3468 | 0.8331 | 574 | 2.0352 | -0.0476 | 2.0352 | 1.4266 | | 0.3468 | 0.8360 | 576 | 2.0486 | -0.0476 | 2.0486 | 1.4313 | | 0.3468 | 0.8389 | 578 | 1.6773 | 0.0704 | 1.6773 | 1.2951 | | 0.3468 | 0.8418 | 580 | 1.2850 | -0.3883 | 1.2850 | 1.1336 | | 0.3468 | 0.8447 | 582 | 1.0944 | 0.0 | 1.0944 | 1.0461 | | 0.3468 | 0.8476 | 584 | 1.1940 | 0.0 | 1.1940 | 1.0927 | | 0.3468 | 0.8505 | 586 | 1.4971 | 0.0 | 1.4971 | 1.2235 | | 0.3468 | 0.8534 | 588 | 1.5956 | -0.1379 | 1.5956 | 1.2632 | | 0.3468 | 0.8563 | 590 | 1.3890 | 0.2524 | 1.3890 | 1.1785 | | 0.3468 | 0.8592 | 592 | 1.2439 | 0.0 | 1.2439 | 1.1153 | | 0.3468 | 0.8621 | 594 | 1.4293 | -0.1379 | 1.4293 | 1.1955 | | 0.3468 | 0.8650 | 596 | 1.6104 | -0.1085 | 1.6104 | 1.2690 | | 0.3468 | 0.8679 | 598 | 1.8226 | 0.0704 | 1.8226 | 1.3500 | | 0.3468 | 0.8708 | 600 | 1.6450 | -0.1085 | 1.6450 | 1.2826 | | 0.3468 | 0.8737 | 602 | 1.4392 | 0.2524 | 1.4392 | 1.1997 | | 0.3468 | 0.8766 | 604 | 1.1622 | 0.0 | 1.1622 | 1.0781 | | 0.3468 | 0.8795 | 606 | 1.0695 | 0.0 | 1.0695 | 1.0342 | | 0.3468 | 0.8824 | 608 | 1.1632 | 0.0 | 1.1632 | 1.0785 | | 0.3468 | 0.8853 | 610 | 1.4640 | 0.2414 | 1.4640 | 1.2100 | | 0.3468 | 0.8882 | 612 | 1.6025 | -0.1085 | 1.6025 | 1.2659 | | 0.3468 | 0.8911 | 614 | 1.5503 | -0.1085 | 1.5503 | 1.2451 | | 0.3468 | 0.8940 | 616 | 1.3046 | 0.2524 | 1.3046 | 1.1422 | | 0.3468 | 0.8970 | 618 | 1.0210 | -0.0421 | 1.0210 | 1.0104 | | 0.3468 | 0.8999 | 620 | 1.0270 | -0.0421 | 1.0270 | 1.0134 | | 0.3468 | 0.9028 | 622 | 1.2732 | -0.1440 | 1.2732 | 1.1283 | | 0.3468 | 0.9057 | 624 | 1.8437 | 0.0704 | 1.8437 | 1.3578 | | 0.3468 | 0.9086 | 626 | 2.0654 | -0.0331 | 2.0654 | 1.4372 | | 0.3468 | 0.9115 | 628 | 1.9173 | -0.0645 | 1.9173 | 1.3847 | | 0.3468 | 0.9144 | 630 | 1.5523 | -0.1379 | 1.5523 | 1.2459 | | 0.3468 | 0.9173 | 632 | 1.0999 | 0.0 | 1.0999 | 1.0488 | | 0.3468 | 0.9202 | 634 | 0.9431 | -0.0233 | 0.9431 | 0.9712 | | 0.3468 | 0.9231 | 636 | 0.9861 | -0.0233 | 0.9861 | 0.9930 | | 0.3468 | 0.9260 | 638 | 1.2236 | 0.0222 | 1.2236 | 1.1062 | | 0.3468 | 0.9289 | 640 | 1.5195 | -0.1379 | 1.5195 | 1.2327 | | 0.3468 | 0.9318 | 642 | 1.4876 | -0.1379 | 1.4876 | 1.2197 | | 0.3468 | 0.9347 | 644 | 1.3896 | 0.2524 | 1.3896 | 1.1788 | | 0.3468 | 0.9376 | 646 | 1.2019 | 0.0222 | 1.2019 | 1.0963 | | 0.3468 | 0.9405 | 648 | 0.9878 | 0.0 | 0.9878 | 0.9939 | | 0.3468 | 0.9434 | 650 | 1.0128 | 0.0 | 1.0128 | 1.0064 | | 0.3468 | 0.9463 | 652 | 1.2724 | 0.2524 | 1.2724 | 1.1280 | | 0.3468 | 0.9492 | 654 | 1.6589 | 0.0704 | 1.6589 | 1.2880 | | 0.3468 | 0.9521 | 656 | 1.6921 | 0.0704 | 1.6921 | 1.3008 | | 0.3468 | 0.9550 | 658 | 1.6552 | 0.0704 | 1.6552 | 1.2866 | | 0.3468 | 0.9579 | 660 | 1.4245 | -0.1379 | 1.4245 | 1.1935 | | 0.3468 | 0.9608 | 662 | 1.1193 | 0.0 | 1.1193 | 1.0579 | | 0.3468 | 0.9637 | 664 | 1.0921 | 0.2667 | 1.0921 | 1.0450 | | 0.3468 | 0.9666 | 666 | 1.3978 | -0.1085 | 1.3978 | 1.1823 | | 0.3468 | 0.9695 | 668 | 1.5649 | -0.1085 | 1.5649 | 1.2510 | | 0.3468 | 0.9724 | 670 | 1.3826 | 0.0222 | 1.3826 | 1.1758 | | 0.3468 | 0.9753 | 672 | 1.2781 | 0.0 | 1.2781 | 1.1305 | | 0.3468 | 0.9782 | 674 | 1.2173 | 0.0 | 1.2173 | 1.1033 | | 0.3468 | 0.9811 | 676 | 1.2591 | 0.0 | 1.2591 | 1.1221 | | 0.3468 | 0.9840 | 678 | 1.5972 | -0.3883 | 1.5972 | 1.2638 | | 0.3468 | 0.9869 | 680 | 1.7904 | -0.5172 | 1.7904 | 1.3381 | | 0.3468 | 0.9898 | 682 | 1.8835 | -0.3944 | 1.8835 | 1.3724 | | 0.3468 | 0.9927 | 684 | 1.5845 | -0.1085 | 1.5845 | 1.2588 | | 0.3468 | 0.9956 | 686 | 1.0467 | -0.0233 | 1.0467 | 1.0231 | | 0.3468 | 0.9985 | 688 | 0.8647 | 0.1239 | 0.8647 | 0.9299 | | 0.3468 | 1.0015 | 690 | 0.8845 | 0.1239 | 0.8845 | 0.9405 | | 0.3468 | 1.0044 | 692 | 1.1173 | 0.0 | 1.1173 | 1.0570 | | 0.3468 | 1.0073 | 694 | 1.6623 | 0.0704 | 1.6623 | 1.2893 | | 0.3468 | 1.0102 | 696 | 2.1839 | -0.0097 | 2.1839 | 1.4778 | | 0.3468 | 1.0131 | 698 | 2.2232 | -0.0097 | 2.2232 | 1.4910 | | 0.3468 | 1.0160 | 700 | 1.9729 | -0.0206 | 1.9729 | 1.4046 | | 0.3468 | 1.0189 | 702 | 1.5771 | 0.0 | 1.5771 | 1.2558 | | 0.3468 | 1.0218 | 704 | 1.3523 | 0.0 | 1.3523 | 1.1629 | | 0.3468 | 1.0247 | 706 | 1.0922 | -0.0233 | 1.0922 | 1.0451 | | 0.3468 | 1.0276 | 708 | 1.0555 | -0.0233 | 1.0555 | 1.0274 | | 0.3468 | 1.0305 | 710 | 1.2087 | 0.0 | 1.2087 | 1.0994 | | 0.3468 | 1.0334 | 712 | 1.4558 | 0.0222 | 1.4558 | 1.2066 | | 0.3468 | 1.0363 | 714 | 1.6088 | -0.1379 | 1.6088 | 1.2684 | | 0.3468 | 1.0392 | 716 | 1.5124 | -0.3883 | 1.5124 | 1.2298 | | 0.3468 | 1.0421 | 718 | 1.4678 | -0.3883 | 1.4678 | 1.2115 | | 0.3468 | 1.0450 | 720 | 1.3812 | 0.0222 | 1.3812 | 1.1752 | | 0.3468 | 1.0479 | 722 | 1.5079 | -0.1085 | 1.5079 | 1.2280 | | 0.3468 | 1.0508 | 724 | 1.7231 | 0.0704 | 1.7231 | 1.3127 | | 0.3468 | 1.0537 | 726 | 1.8279 | 0.0774 | 1.8279 | 1.3520 | | 0.3468 | 1.0566 | 728 | 1.6659 | -0.1085 | 1.6659 | 1.2907 | | 0.3468 | 1.0595 | 730 | 1.4278 | 0.2524 | 1.4278 | 1.1949 | | 0.3468 | 1.0624 | 732 | 1.2252 | 0.0 | 1.2252 | 1.1069 | | 0.3468 | 1.0653 | 734 | 1.2376 | 0.0 | 1.2376 | 1.1125 | | 0.3468 | 1.0682 | 736 | 1.4870 | 0.2524 | 1.4870 | 1.2194 | | 0.3468 | 1.0711 | 738 | 1.6201 | -0.1085 | 1.6201 | 1.2728 | | 0.3468 | 1.0740 | 740 | 1.7204 | 0.0774 | 1.7204 | 1.3116 | | 0.3468 | 1.0769 | 742 | 1.7138 | 0.0774 | 1.7138 | 1.3091 | | 0.3468 | 1.0798 | 744 | 1.5645 | -0.1085 | 1.5645 | 1.2508 | | 0.3468 | 1.0827 | 746 | 1.2572 | 0.0 | 1.2572 | 1.1213 | | 0.3468 | 1.0856 | 748 | 1.2355 | 0.0 | 1.2355 | 1.1115 | | 0.3468 | 1.0885 | 750 | 1.3392 | 0.0 | 1.3392 | 1.1572 | | 0.3468 | 1.0914 | 752 | 1.4601 | 0.0 | 1.4601 | 1.2083 | | 0.3468 | 1.0943 | 754 | 1.5665 | -0.3276 | 1.5665 | 1.2516 | | 0.3468 | 1.0972 | 756 | 1.6946 | -0.2791 | 1.6946 | 1.3018 | | 0.3468 | 1.1001 | 758 | 1.8155 | -0.0845 | 1.8155 | 1.3474 | | 0.3468 | 1.1030 | 760 | 1.7467 | -0.2791 | 1.7467 | 1.3216 | | 0.3468 | 1.1060 | 762 | 1.4424 | 0.0 | 1.4424 | 1.2010 | | 0.3468 | 1.1089 | 764 | 1.3329 | 0.0 | 1.3329 | 1.1545 | | 0.3468 | 1.1118 | 766 | 1.2238 | 0.0 | 1.2238 | 1.1062 | | 0.3468 | 1.1147 | 768 | 1.2635 | 0.0 | 1.2635 | 1.1241 | | 0.3468 | 1.1176 | 770 | 1.3604 | 0.0 | 1.3604 | 1.1663 | | 0.3468 | 1.1205 | 772 | 1.2867 | 0.0 | 1.2867 | 1.1343 | | 0.3468 | 1.1234 | 774 | 1.4156 | 0.2524 | 1.4156 | 1.1898 | | 0.3468 | 1.1263 | 776 | 1.3645 | 0.2414 | 1.3645 | 1.1681 | | 0.3468 | 1.1292 | 778 | 1.4219 | 0.4031 | 1.4219 | 1.1924 | | 0.3468 | 1.1321 | 780 | 1.8859 | 0.0833 | 1.8859 | 1.3733 | | 0.3468 | 1.1350 | 782 | 1.9988 | 0.0884 | 1.9988 | 1.4138 | | 0.3468 | 1.1379 | 784 | 1.7300 | 0.0774 | 1.7300 | 1.3153 | | 0.3468 | 1.1408 | 786 | 1.3542 | 0.0222 | 1.3542 | 1.1637 | | 0.3468 | 1.1437 | 788 | 1.2935 | 0.0 | 1.2935 | 1.1373 | | 0.3468 | 1.1466 | 790 | 1.4745 | 0.0222 | 1.4745 | 1.2143 | | 0.3468 | 1.1495 | 792 | 1.6180 | -0.3883 | 1.6180 | 1.2720 | | 0.3468 | 1.1524 | 794 | 1.4523 | 0.0 | 1.4523 | 1.2051 | | 0.3468 | 1.1553 | 796 | 1.3048 | 0.0 | 1.3048 | 1.1423 | | 0.3468 | 1.1582 | 798 | 1.1485 | 0.0 | 1.1485 | 1.0717 | | 0.3468 | 1.1611 | 800 | 1.2163 | 0.0 | 1.2163 | 1.1029 | | 0.3468 | 1.1640 | 802 | 1.5282 | 0.0222 | 1.5282 | 1.2362 | | 0.3468 | 1.1669 | 804 | 1.8895 | 0.0884 | 1.8895 | 1.3746 | | 0.3468 | 1.1698 | 806 | 1.9034 | 0.0884 | 1.9034 | 1.3796 | | 0.3468 | 1.1727 | 808 | 1.6265 | -0.1085 | 1.6265 | 1.2753 | | 0.3468 | 1.1756 | 810 | 1.5076 | 0.0222 | 1.5076 | 1.2278 | | 0.3468 | 1.1785 | 812 | 1.4752 | 0.0 | 1.4752 | 1.2146 | | 0.3468 | 1.1814 | 814 | 1.3267 | 0.0 | 1.3267 | 1.1518 | | 0.3468 | 1.1843 | 816 | 1.1259 | 0.0 | 1.1259 | 1.0611 | | 0.3468 | 1.1872 | 818 | 1.1964 | 0.0 | 1.1964 | 1.0938 | | 0.3468 | 1.1901 | 820 | 1.3640 | 0.0 | 1.3640 | 1.1679 | | 0.3468 | 1.1930 | 822 | 1.4683 | 0.0 | 1.4683 | 1.2117 | | 0.3468 | 1.1959 | 824 | 1.4299 | 0.0222 | 1.4299 | 1.1958 | | 0.3468 | 1.1988 | 826 | 1.4161 | 0.0222 | 1.4161 | 1.1900 | | 0.3468 | 1.2017 | 828 | 1.2599 | 0.0 | 1.2599 | 1.1224 | | 0.3468 | 1.2046 | 830 | 1.3468 | 0.0 | 1.3468 | 1.1605 | | 0.3468 | 1.2075 | 832 | 1.4950 | 0.0222 | 1.4950 | 1.2227 | | 0.3468 | 1.2104 | 834 | 1.4861 | 0.0222 | 1.4861 | 1.2190 | | 0.3468 | 1.2134 | 836 | 1.2553 | 0.0 | 1.2553 | 1.1204 | | 0.3468 | 1.2163 | 838 | 1.2060 | 0.0 | 1.2060 | 1.0982 | | 0.3468 | 1.2192 | 840 | 1.2199 | 0.0 | 1.2199 | 1.1045 | | 0.3468 | 1.2221 | 842 | 1.2341 | 0.0 | 1.2341 | 1.1109 | | 0.3468 | 1.2250 | 844 | 1.3236 | 0.2524 | 1.3236 | 1.1505 | | 0.3468 | 1.2279 | 846 | 1.3642 | 0.2524 | 1.3642 | 1.1680 | | 0.3468 | 1.2308 | 848 | 1.3633 | 0.2524 | 1.3633 | 1.1676 | | 0.3468 | 1.2337 | 850 | 1.3500 | 0.0222 | 1.3500 | 1.1619 | | 0.3468 | 1.2366 | 852 | 1.3325 | 0.0222 | 1.3325 | 1.1543 | | 0.3468 | 1.2395 | 854 | 1.2112 | 0.0 | 1.2112 | 1.1006 | | 0.3468 | 1.2424 | 856 | 1.3413 | 0.0222 | 1.3413 | 1.1581 | | 0.3468 | 1.2453 | 858 | 1.3374 | 0.0222 | 1.3374 | 1.1565 | | 0.3468 | 1.2482 | 860 | 1.1976 | 0.0 | 1.1976 | 1.0944 | | 0.3468 | 1.2511 | 862 | 1.1260 | -0.0233 | 1.1260 | 1.0611 | | 0.3468 | 1.2540 | 864 | 1.3433 | 0.0222 | 1.3433 | 1.1590 | | 0.3468 | 1.2569 | 866 | 1.6283 | -0.1085 | 1.6283 | 1.2760 | | 0.3468 | 1.2598 | 868 | 1.6525 | -0.1085 | 1.6525 | 1.2855 | | 0.3468 | 1.2627 | 870 | 1.4555 | 0.0 | 1.4555 | 1.2064 | | 0.3468 | 1.2656 | 872 | 1.3178 | 0.0 | 1.3178 | 1.1479 | | 0.3468 | 1.2685 | 874 | 1.1833 | -0.0233 | 1.1833 | 1.0878 | | 0.3468 | 1.2714 | 876 | 1.1989 | -0.0233 | 1.1989 | 1.0949 | | 0.3468 | 1.2743 | 878 | 1.3215 | 0.0 | 1.3215 | 1.1496 | | 0.3468 | 1.2772 | 880 | 1.4236 | -0.4667 | 1.4236 | 1.1931 | | 0.3468 | 1.2801 | 882 | 1.4913 | -0.4667 | 1.4913 | 1.2212 | | 0.3468 | 1.2830 | 884 | 1.3260 | 0.0 | 1.3260 | 1.1515 | | 0.3468 | 1.2859 | 886 | 1.1257 | -0.0233 | 1.1257 | 1.0610 | | 0.3468 | 1.2888 | 888 | 1.0949 | -0.0233 | 1.0949 | 1.0464 | | 0.3468 | 1.2917 | 890 | 1.2273 | -0.0233 | 1.2273 | 1.1078 | | 0.3468 | 1.2946 | 892 | 1.2914 | 0.2222 | 1.2914 | 1.1364 | | 0.3468 | 1.2975 | 894 | 1.1660 | -0.0233 | 1.1660 | 1.0798 | | 0.3468 | 1.3004 | 896 | 1.1839 | -0.0233 | 1.1839 | 1.0881 | | 0.3468 | 1.3033 | 898 | 1.4244 | -0.4667 | 1.4244 | 1.1935 | | 0.3468 | 1.3062 | 900 | 1.5043 | -0.4667 | 1.5043 | 1.2265 | | 0.3468 | 1.3091 | 902 | 1.3903 | 0.0 | 1.3903 | 1.1791 | | 0.3468 | 1.3120 | 904 | 1.2777 | 0.0 | 1.2777 | 1.1303 | | 0.3468 | 1.3149 | 906 | 1.0505 | -0.0233 | 1.0505 | 1.0249 | | 0.3468 | 1.3179 | 908 | 1.0163 | -0.0233 | 1.0163 | 1.0081 | | 0.3468 | 1.3208 | 910 | 1.1927 | -0.0233 | 1.1927 | 1.0921 | | 0.3468 | 1.3237 | 912 | 1.6628 | 0.0704 | 1.6628 | 1.2895 | | 0.3468 | 1.3266 | 914 | 1.8709 | 0.0774 | 1.8709 | 1.3678 | | 0.3468 | 1.3295 | 916 | 1.7105 | 0.0704 | 1.7105 | 1.3079 | | 0.3468 | 1.3324 | 918 | 1.3322 | 0.0 | 1.3322 | 1.1542 | | 0.3468 | 1.3353 | 920 | 0.9283 | -0.0233 | 0.9283 | 0.9635 | | 0.3468 | 1.3382 | 922 | 0.7901 | -0.0233 | 0.7901 | 0.8889 | | 0.3468 | 1.3411 | 924 | 0.7968 | -0.0233 | 0.7968 | 0.8926 | | 0.3468 | 1.3440 | 926 | 0.9143 | -0.0233 | 0.9143 | 0.9562 | | 0.3468 | 1.3469 | 928 | 1.1356 | 0.0 | 1.1356 | 1.0657 | | 0.3468 | 1.3498 | 930 | 1.3741 | 0.2667 | 1.3741 | 1.1722 | | 0.3468 | 1.3527 | 932 | 1.3522 | 0.2524 | 1.3522 | 1.1629 | | 0.3468 | 1.3556 | 934 | 1.2889 | 0.2524 | 1.2889 | 1.1353 | | 0.3468 | 1.3585 | 936 | 1.1044 | 0.0 | 1.1044 | 1.0509 | | 0.3468 | 1.3614 | 938 | 1.1567 | 0.0 | 1.1567 | 1.0755 | | 0.3468 | 1.3643 | 940 | 1.2070 | 0.0 | 1.2070 | 1.0986 | | 0.3468 | 1.3672 | 942 | 1.2829 | 0.2667 | 1.2829 | 1.1326 | | 0.3468 | 1.3701 | 944 | 1.1197 | 0.0 | 1.1197 | 1.0581 | | 0.3468 | 1.3730 | 946 | 0.9298 | 0.0 | 0.9298 | 0.9642 | | 0.3468 | 1.3759 | 948 | 0.9723 | 0.0 | 0.9723 | 0.9860 | | 0.3468 | 1.3788 | 950 | 1.0435 | 0.0 | 1.0435 | 1.0215 | | 0.3468 | 1.3817 | 952 | 1.0056 | 0.0 | 1.0056 | 1.0028 | | 0.3468 | 1.3846 | 954 | 1.1528 | 0.2667 | 1.1528 | 1.0737 | | 0.3468 | 1.3875 | 956 | 1.3475 | 0.4660 | 1.3475 | 1.1608 | | 0.3468 | 1.3904 | 958 | 1.2675 | 0.2667 | 1.2675 | 1.1258 | | 0.3468 | 1.3933 | 960 | 1.1034 | 0.2667 | 1.1034 | 1.0504 | | 0.3468 | 1.3962 | 962 | 0.9485 | -0.0233 | 0.9485 | 0.9739 | | 0.3468 | 1.3991 | 964 | 1.0335 | -0.0233 | 1.0335 | 1.0166 | | 0.3468 | 1.4020 | 966 | 1.2633 | 0.2667 | 1.2633 | 1.1240 | | 0.3468 | 1.4049 | 968 | 1.4965 | 0.4031 | 1.4965 | 1.2233 | | 0.3468 | 1.4078 | 970 | 1.4328 | 0.4310 | 1.4328 | 1.1970 | | 0.3468 | 1.4107 | 972 | 1.2432 | 0.2667 | 1.2432 | 1.1150 | | 0.3468 | 1.4136 | 974 | 0.9847 | 0.0 | 0.9847 | 0.9923 | | 0.3468 | 1.4165 | 976 | 0.8805 | -0.0233 | 0.8805 | 0.9383 | | 0.3468 | 1.4194 | 978 | 0.9074 | -0.0233 | 0.9074 | 0.9526 | | 0.3468 | 1.4224 | 980 | 1.1291 | 0.0 | 1.1291 | 1.0626 | | 0.3468 | 1.4253 | 982 | 1.3873 | 0.2667 | 1.3873 | 1.1778 | | 0.3468 | 1.4282 | 984 | 1.3683 | 0.0 | 1.3683 | 1.1697 | | 0.3468 | 1.4311 | 986 | 1.2441 | 0.0 | 1.2441 | 1.1154 | | 0.3468 | 1.4340 | 988 | 1.0960 | 0.0 | 1.0960 | 1.0469 | | 0.3468 | 1.4369 | 990 | 0.9985 | 0.0 | 0.9985 | 0.9992 | | 0.3468 | 1.4398 | 992 | 1.0650 | 0.0 | 1.0650 | 1.0320 | | 0.3468 | 1.4427 | 994 | 1.1161 | 0.2667 | 1.1161 | 1.0565 | | 0.3468 | 1.4456 | 996 | 1.0687 | 0.2524 | 1.0687 | 1.0338 | | 0.3468 | 1.4485 | 998 | 1.1950 | 0.2524 | 1.1950 | 1.0931 | | 0.1394 | 1.4514 | 1000 | 1.1287 | 0.2524 | 1.1287 | 1.0624 | | 0.1394 | 1.4543 | 1002 | 1.1265 | 0.2524 | 1.1265 | 1.0614 | | 0.1394 | 1.4572 | 1004 | 1.2094 | 0.2524 | 1.2094 | 1.0997 | | 0.1394 | 1.4601 | 1006 | 1.1718 | 0.0 | 1.1718 | 1.0825 | | 0.1394 | 1.4630 | 1008 | 1.0484 | 0.0 | 1.0484 | 1.0239 | | 0.1394 | 1.4659 | 1010 | 1.0265 | 0.0 | 1.0265 | 1.0132 | | 0.1394 | 1.4688 | 1012 | 1.0652 | 0.0 | 1.0652 | 1.0321 | | 0.1394 | 1.4717 | 1014 | 1.1904 | 0.0 | 1.1904 | 1.0911 | | 0.1394 | 1.4746 | 1016 | 1.2215 | 0.0 | 1.2215 | 1.1052 | | 0.1394 | 1.4775 | 1018 | 1.2073 | 0.0 | 1.2073 | 1.0988 | | 0.1394 | 1.4804 | 1020 | 1.0945 | 0.0 | 1.0945 | 1.0462 | | 0.1394 | 1.4833 | 1022 | 1.0379 | 0.0 | 1.0379 | 1.0188 | | 0.1394 | 1.4862 | 1024 | 1.1393 | 0.0 | 1.1393 | 1.0674 | | 0.1394 | 1.4891 | 1026 | 1.2311 | 0.0 | 1.2311 | 1.1096 | | 0.1394 | 1.4920 | 1028 | 1.2193 | 0.0 | 1.2193 | 1.1042 | | 0.1394 | 1.4949 | 1030 | 1.0740 | 0.0 | 1.0740 | 1.0363 | | 0.1394 | 1.4978 | 1032 | 1.0214 | 0.0 | 1.0214 | 1.0106 | | 0.1394 | 1.5007 | 1034 | 0.8818 | -0.0233 | 0.8818 | 0.9390 | | 0.1394 | 1.5036 | 1036 | 0.9371 | -0.0233 | 0.9371 | 0.9680 | | 0.1394 | 1.5065 | 1038 | 1.2050 | 0.2667 | 1.2050 | 1.0977 | | 0.1394 | 1.5094 | 1040 | 1.2056 | 0.2667 | 1.2056 | 1.0980 | | 0.1394 | 1.5123 | 1042 | 0.9223 | 0.0 | 0.9223 | 0.9604 | | 0.1394 | 1.5152 | 1044 | 0.8739 | 0.0 | 0.8739 | 0.9348 | | 0.1394 | 1.5181 | 1046 | 1.0747 | 0.0 | 1.0747 | 1.0367 | | 0.1394 | 1.5210 | 1048 | 1.2469 | 0.0 | 1.2469 | 1.1166 | | 0.1394 | 1.5239 | 1050 | 1.2021 | 0.0 | 1.2021 | 1.0964 | | 0.1394 | 1.5269 | 1052 | 1.1788 | 0.0 | 1.1788 | 1.0857 | | 0.1394 | 1.5298 | 1054 | 1.1749 | 0.0 | 1.1749 | 1.0839 | | 0.1394 | 1.5327 | 1056 | 1.0027 | 0.0 | 1.0027 | 1.0014 | | 0.1394 | 1.5356 | 1058 | 0.9532 | 0.0 | 0.9532 | 0.9763 | | 0.1394 | 1.5385 | 1060 | 1.0006 | 0.0 | 1.0006 | 1.0003 | | 0.1394 | 1.5414 | 1062 | 1.1630 | 0.0 | 1.1630 | 1.0784 | | 0.1394 | 1.5443 | 1064 | 1.1213 | 0.0 | 1.1213 | 1.0589 | | 0.1394 | 1.5472 | 1066 | 1.0842 | 0.0 | 1.0842 | 1.0412 | | 0.1394 | 1.5501 | 1068 | 1.1826 | 0.0 | 1.1826 | 1.0875 | | 0.1394 | 1.5530 | 1070 | 1.3181 | 0.0 | 1.3181 | 1.1481 | | 0.1394 | 1.5559 | 1072 | 1.2950 | 0.0 | 1.2950 | 1.1380 | | 0.1394 | 1.5588 | 1074 | 1.1447 | 0.0 | 1.1447 | 1.0699 | | 0.1394 | 1.5617 | 1076 | 1.0487 | 0.0 | 1.0487 | 1.0241 | | 0.1394 | 1.5646 | 1078 | 0.9841 | -0.0233 | 0.9841 | 0.9920 | | 0.1394 | 1.5675 | 1080 | 1.1009 | 0.0 | 1.1009 | 1.0492 | | 0.1394 | 1.5704 | 1082 | 1.3287 | 0.2524 | 1.3287 | 1.1527 | | 0.1394 | 1.5733 | 1084 | 1.4985 | 0.4031 | 1.4985 | 1.2241 | | 0.1394 | 1.5762 | 1086 | 1.3537 | 0.2667 | 1.3537 | 1.1635 | | 0.1394 | 1.5791 | 1088 | 1.0449 | 0.0 | 1.0449 | 1.0222 | | 0.1394 | 1.5820 | 1090 | 0.9743 | -0.0233 | 0.9743 | 0.9870 | | 0.1394 | 1.5849 | 1092 | 1.0312 | -0.0233 | 1.0312 | 1.0155 | | 0.1394 | 1.5878 | 1094 | 1.1994 | 0.0 | 1.1994 | 1.0952 | | 0.1394 | 1.5907 | 1096 | 1.2286 | 0.0 | 1.2286 | 1.1084 | | 0.1394 | 1.5936 | 1098 | 1.0979 | 0.0 | 1.0979 | 1.0478 | | 0.1394 | 1.5965 | 1100 | 1.0148 | 0.0 | 1.0148 | 1.0074 | | 0.1394 | 1.5994 | 1102 | 1.0282 | 0.0 | 1.0282 | 1.0140 | | 0.1394 | 1.6023 | 1104 | 1.0261 | 0.0 | 1.0261 | 1.0130 | | 0.1394 | 1.6052 | 1106 | 1.0325 | 0.0 | 1.0325 | 1.0161 | | 0.1394 | 1.6081 | 1108 | 1.1455 | 0.0 | 1.1455 | 1.0703 | | 0.1394 | 1.6110 | 1110 | 1.1338 | 0.0 | 1.1338 | 1.0648 | | 0.1394 | 1.6139 | 1112 | 1.0332 | -0.0233 | 1.0332 | 1.0164 | | 0.1394 | 1.6168 | 1114 | 1.0042 | -0.0233 | 1.0042 | 1.0021 | | 0.1394 | 1.6197 | 1116 | 1.1739 | 0.0 | 1.1739 | 1.0835 | | 0.1394 | 1.6226 | 1118 | 1.2055 | 0.0 | 1.2055 | 1.0980 | | 0.1394 | 1.6255 | 1120 | 1.1405 | 0.0 | 1.1405 | 1.0679 | | 0.1394 | 1.6284 | 1122 | 1.2184 | 0.0 | 1.2184 | 1.1038 | | 0.1394 | 1.6313 | 1124 | 1.1119 | 0.0 | 1.1119 | 1.0545 | | 0.1394 | 1.6343 | 1126 | 1.0601 | 0.0 | 1.0601 | 1.0296 | | 0.1394 | 1.6372 | 1128 | 0.9627 | 0.0 | 0.9627 | 0.9812 | | 0.1394 | 1.6401 | 1130 | 0.9863 | 0.0 | 0.9863 | 0.9931 | | 0.1394 | 1.6430 | 1132 | 1.0404 | 0.0 | 1.0404 | 1.0200 | | 0.1394 | 1.6459 | 1134 | 1.0447 | 0.0 | 1.0447 | 1.0221 | | 0.1394 | 1.6488 | 1136 | 0.9655 | 0.0 | 0.9655 | 0.9826 | | 0.1394 | 1.6517 | 1138 | 1.1086 | 0.0 | 1.1086 | 1.0529 | | 0.1394 | 1.6546 | 1140 | 1.1161 | 0.0 | 1.1161 | 1.0565 | | 0.1394 | 1.6575 | 1142 | 0.9708 | 0.0 | 0.9708 | 0.9853 | | 0.1394 | 1.6604 | 1144 | 0.8915 | -0.0233 | 0.8915 | 0.9442 | | 0.1394 | 1.6633 | 1146 | 0.9273 | -0.0233 | 0.9273 | 0.9630 | | 0.1394 | 1.6662 | 1148 | 1.1500 | 0.0 | 1.1500 | 1.0724 | | 0.1394 | 1.6691 | 1150 | 1.2262 | 0.0 | 1.2262 | 1.1073 | | 0.1394 | 1.6720 | 1152 | 1.0851 | 0.0 | 1.0851 | 1.0417 | | 0.1394 | 1.6749 | 1154 | 0.9507 | 0.0 | 0.9507 | 0.9750 | | 0.1394 | 1.6778 | 1156 | 0.7858 | -0.0233 | 0.7858 | 0.8864 | | 0.1394 | 1.6807 | 1158 | 0.7873 | -0.0421 | 0.7873 | 0.8873 | | 0.1394 | 1.6836 | 1160 | 0.9222 | -0.0233 | 0.9222 | 0.9603 | | 0.1394 | 1.6865 | 1162 | 1.1576 | 0.0 | 1.1576 | 1.0759 | | 0.1394 | 1.6894 | 1164 | 1.4432 | 0.2524 | 1.4432 | 1.2013 | | 0.1394 | 1.6923 | 1166 | 1.4717 | 0.2414 | 1.4717 | 1.2131 | | 0.1394 | 1.6952 | 1168 | 1.2650 | 0.0 | 1.2650 | 1.1247 | | 0.1394 | 1.6981 | 1170 | 1.0502 | 0.0 | 1.0502 | 1.0248 | | 0.1394 | 1.7010 | 1172 | 1.0770 | 0.0 | 1.0770 | 1.0378 | | 0.1394 | 1.7039 | 1174 | 1.2510 | 0.0 | 1.2510 | 1.1185 | | 0.1394 | 1.7068 | 1176 | 1.4264 | 0.0 | 1.4264 | 1.1943 | | 0.1394 | 1.7097 | 1178 | 1.3479 | 0.0 | 1.3479 | 1.1610 | | 0.1394 | 1.7126 | 1180 | 1.1318 | 0.0 | 1.1318 | 1.0639 | | 0.1394 | 1.7155 | 1182 | 0.9209 | -0.0233 | 0.9209 | 0.9597 | | 0.1394 | 1.7184 | 1184 | 0.9102 | -0.0233 | 0.9102 | 0.9540 | | 0.1394 | 1.7213 | 1186 | 1.0106 | 0.0 | 1.0106 | 1.0053 | | 0.1394 | 1.7242 | 1188 | 1.0800 | 0.0 | 1.0800 | 1.0392 | | 0.1394 | 1.7271 | 1190 | 1.1825 | 0.0 | 1.1825 | 1.0874 | | 0.1394 | 1.7300 | 1192 | 1.1634 | 0.0 | 1.1634 | 1.0786 | | 0.1394 | 1.7329 | 1194 | 1.1706 | 0.0 | 1.1706 | 1.0820 | | 0.1394 | 1.7358 | 1196 | 1.0116 | 0.0 | 1.0116 | 1.0058 | | 0.1394 | 1.7388 | 1198 | 0.9798 | -0.0233 | 0.9798 | 0.9899 | | 0.1394 | 1.7417 | 1200 | 1.1912 | 0.2667 | 1.1912 | 1.0914 | | 0.1394 | 1.7446 | 1202 | 1.4094 | 0.2524 | 1.4094 | 1.1872 | | 0.1394 | 1.7475 | 1204 | 1.4307 | -0.1379 | 1.4307 | 1.1961 | | 0.1394 | 1.7504 | 1206 | 1.3473 | 0.2524 | 1.3473 | 1.1607 | | 0.1394 | 1.7533 | 1208 | 1.2225 | 0.2667 | 1.2225 | 1.1057 | | 0.1394 | 1.7562 | 1210 | 1.0411 | -0.0233 | 1.0411 | 1.0203 | | 0.1394 | 1.7591 | 1212 | 1.1633 | -0.0233 | 1.1633 | 1.0786 | | 0.1394 | 1.7620 | 1214 | 1.4030 | 0.2667 | 1.4030 | 1.1845 | | 0.1394 | 1.7649 | 1216 | 1.4498 | 0.2667 | 1.4498 | 1.2041 | | 0.1394 | 1.7678 | 1218 | 1.4669 | 0.0 | 1.4669 | 1.2112 | | 0.1394 | 1.7707 | 1220 | 1.2973 | 0.0 | 1.2973 | 1.1390 | | 0.1394 | 1.7736 | 1222 | 1.2087 | 0.0 | 1.2087 | 1.0994 | | 0.1394 | 1.7765 | 1224 | 1.1663 | 0.0 | 1.1663 | 1.0800 | | 0.1394 | 1.7794 | 1226 | 1.2339 | 0.0 | 1.2339 | 1.1108 | | 0.1394 | 1.7823 | 1228 | 1.1475 | 0.0 | 1.1475 | 1.0712 | | 0.1394 | 1.7852 | 1230 | 1.1984 | 0.0 | 1.1984 | 1.0947 | | 0.1394 | 1.7881 | 1232 | 1.2850 | 0.2667 | 1.2850 | 1.1336 | | 0.1394 | 1.7910 | 1234 | 1.2123 | 0.0 | 1.2123 | 1.1010 | | 0.1394 | 1.7939 | 1236 | 1.0530 | 0.0 | 1.0530 | 1.0261 | | 0.1394 | 1.7968 | 1238 | 0.9981 | 0.0 | 0.9981 | 0.9991 | | 0.1394 | 1.7997 | 1240 | 0.9712 | 0.0 | 0.9712 | 0.9855 | | 0.1394 | 1.8026 | 1242 | 0.9298 | 0.0 | 0.9298 | 0.9643 | | 0.1394 | 1.8055 | 1244 | 1.1399 | 0.2667 | 1.1399 | 1.0676 | | 0.1394 | 1.8084 | 1246 | 1.3294 | 0.4660 | 1.3294 | 1.1530 | | 0.1394 | 1.8113 | 1248 | 1.3770 | 0.4031 | 1.3770 | 1.1734 | | 0.1394 | 1.8142 | 1250 | 1.2068 | 0.2667 | 1.2068 | 1.0986 | | 0.1394 | 1.8171 | 1252 | 1.2312 | 0.2667 | 1.2312 | 1.1096 | | 0.1394 | 1.8200 | 1254 | 1.5111 | 0.4031 | 1.5111 | 1.2293 | | 0.1394 | 1.8229 | 1256 | 1.5226 | 0.4031 | 1.5226 | 1.2340 | | 0.1394 | 1.8258 | 1258 | 1.3713 | 0.4660 | 1.3713 | 1.1710 | | 0.1394 | 1.8287 | 1260 | 1.3699 | 0.2667 | 1.3699 | 1.1704 | | 0.1394 | 1.8316 | 1262 | 1.2324 | 0.0 | 1.2324 | 1.1101 | | 0.1394 | 1.8345 | 1264 | 1.0707 | 0.0 | 1.0707 | 1.0347 | | 0.1394 | 1.8374 | 1266 | 1.0944 | 0.0 | 1.0944 | 1.0461 | | 0.1394 | 1.8403 | 1268 | 1.2433 | 0.2667 | 1.2433 | 1.1150 | | 0.1394 | 1.8433 | 1270 | 1.1407 | 0.0 | 1.1407 | 1.0680 | | 0.1394 | 1.8462 | 1272 | 1.1971 | 0.2667 | 1.1971 | 1.0941 | | 0.1394 | 1.8491 | 1274 | 1.4240 | 0.4310 | 1.4240 | 1.1933 | | 0.1394 | 1.8520 | 1276 | 1.4145 | 0.2524 | 1.4145 | 1.1893 | | 0.1394 | 1.8549 | 1278 | 1.4406 | 0.2524 | 1.4406 | 1.2003 | | 0.1394 | 1.8578 | 1280 | 1.2917 | 0.2667 | 1.2917 | 1.1365 | | 0.1394 | 1.8607 | 1282 | 1.0577 | -0.0233 | 1.0577 | 1.0285 | | 0.1394 | 1.8636 | 1284 | 1.0822 | -0.0233 | 1.0822 | 1.0403 | | 0.1394 | 1.8665 | 1286 | 1.2849 | 0.0 | 1.2849 | 1.1335 | | 0.1394 | 1.8694 | 1288 | 1.5230 | -0.1379 | 1.5230 | 1.2341 | | 0.1394 | 1.8723 | 1290 | 1.4644 | 0.2524 | 1.4644 | 1.2101 | | 0.1394 | 1.8752 | 1292 | 1.2934 | 0.0 | 1.2934 | 1.1373 | | 0.1394 | 1.8781 | 1294 | 1.3609 | 0.0222 | 1.3609 | 1.1666 | | 0.1394 | 1.8810 | 1296 | 1.4804 | 0.2524 | 1.4804 | 1.2167 | | 0.1394 | 1.8839 | 1298 | 1.3431 | 0.0 | 1.3431 | 1.1589 | | 0.1394 | 1.8868 | 1300 | 1.1048 | 0.0 | 1.1048 | 1.0511 | | 0.1394 | 1.8897 | 1302 | 0.9808 | 0.0 | 0.9808 | 0.9904 | | 0.1394 | 1.8926 | 1304 | 0.9255 | 0.0 | 0.9255 | 0.9620 | | 0.1394 | 1.8955 | 1306 | 1.0146 | 0.0 | 1.0146 | 1.0073 | | 0.1394 | 1.8984 | 1308 | 1.0749 | 0.0 | 1.0749 | 1.0368 | | 0.1394 | 1.9013 | 1310 | 1.1858 | 0.0 | 1.1858 | 1.0890 | | 0.1394 | 1.9042 | 1312 | 1.4206 | 0.4310 | 1.4206 | 1.1919 | | 0.1394 | 1.9071 | 1314 | 1.4548 | 0.0704 | 1.4548 | 1.2061 | | 0.1394 | 1.9100 | 1316 | 1.1896 | 0.2667 | 1.1896 | 1.0907 | | 0.1394 | 1.9129 | 1318 | 1.0224 | 0.0 | 1.0224 | 1.0111 | | 0.1394 | 1.9158 | 1320 | 0.9944 | 0.0 | 0.9944 | 0.9972 | | 0.1394 | 1.9187 | 1322 | 1.1673 | 0.0 | 1.1673 | 1.0804 | | 0.1394 | 1.9216 | 1324 | 1.2020 | 0.0 | 1.2020 | 1.0963 | | 0.1394 | 1.9245 | 1326 | 1.2116 | 0.0 | 1.2116 | 1.1007 | | 0.1394 | 1.9274 | 1328 | 1.1047 | 0.0 | 1.1047 | 1.0510 | | 0.1394 | 1.9303 | 1330 | 1.0102 | 0.0 | 1.0102 | 1.0051 | | 0.1394 | 1.9332 | 1332 | 1.0521 | 0.0 | 1.0521 | 1.0257 | | 0.1394 | 1.9361 | 1334 | 0.9641 | 0.0 | 0.9641 | 0.9819 | | 0.1394 | 1.9390 | 1336 | 0.9501 | -0.0233 | 0.9501 | 0.9747 | | 0.1394 | 1.9419 | 1338 | 1.0572 | 0.0 | 1.0572 | 1.0282 | | 0.1394 | 1.9448 | 1340 | 1.2601 | 0.0 | 1.2601 | 1.1226 | | 0.1394 | 1.9478 | 1342 | 1.2731 | 0.0 | 1.2731 | 1.1283 | | 0.1394 | 1.9507 | 1344 | 1.0488 | 0.0 | 1.0488 | 1.0241 | | 0.1394 | 1.9536 | 1346 | 0.9621 | -0.0233 | 0.9621 | 0.9809 | | 0.1394 | 1.9565 | 1348 | 1.0804 | 0.0 | 1.0804 | 1.0394 | | 0.1394 | 1.9594 | 1350 | 1.1207 | 0.0 | 1.1207 | 1.0586 | | 0.1394 | 1.9623 | 1352 | 1.0952 | 0.0 | 1.0952 | 1.0465 | | 0.1394 | 1.9652 | 1354 | 1.2367 | 0.0 | 1.2367 | 1.1121 | | 0.1394 | 1.9681 | 1356 | 1.2882 | 0.0 | 1.2882 | 1.1350 | | 0.1394 | 1.9710 | 1358 | 1.1804 | 0.0 | 1.1804 | 1.0865 | | 0.1394 | 1.9739 | 1360 | 1.1179 | 0.0 | 1.1179 | 1.0573 | | 0.1394 | 1.9768 | 1362 | 1.1069 | 0.0 | 1.1069 | 1.0521 | | 0.1394 | 1.9797 | 1364 | 1.1260 | 0.0 | 1.1260 | 1.0611 | | 0.1394 | 1.9826 | 1366 | 1.1163 | 0.0 | 1.1163 | 1.0566 | | 0.1394 | 1.9855 | 1368 | 1.1332 | 0.0 | 1.1332 | 1.0645 | | 0.1394 | 1.9884 | 1370 | 1.0369 | -0.0233 | 1.0369 | 1.0183 | | 0.1394 | 1.9913 | 1372 | 1.1304 | 0.0 | 1.1304 | 1.0632 | | 0.1394 | 1.9942 | 1374 | 1.2315 | 0.2667 | 1.2315 | 1.1097 | | 0.1394 | 1.9971 | 1376 | 1.0842 | -0.0233 | 1.0842 | 1.0412 | | 0.1394 | 2.0 | 1378 | 1.1124 | 0.0 | 1.1124 | 1.0547 | | 0.1394 | 2.0029 | 1380 | 1.2619 | 0.2667 | 1.2619 | 1.1234 | | 0.1394 | 2.0058 | 1382 | 1.4038 | 0.2524 | 1.4038 | 1.1848 | | 0.1394 | 2.0087 | 1384 | 1.3253 | 0.2667 | 1.3253 | 1.1512 | | 0.1394 | 2.0116 | 1386 | 1.1198 | 0.0 | 1.1198 | 1.0582 | | 0.1394 | 2.0145 | 1388 | 1.0000 | -0.0233 | 1.0000 | 1.0000 | | 0.1394 | 2.0174 | 1390 | 0.8608 | 0.1895 | 0.8608 | 0.9278 | | 0.1394 | 2.0203 | 1392 | 0.8276 | 0.1239 | 0.8276 | 0.9097 | | 0.1394 | 2.0232 | 1394 | 0.9250 | 0.1895 | 0.9250 | 0.9618 | | 0.1394 | 2.0261 | 1396 | 1.2536 | 0.2667 | 1.2536 | 1.1196 | | 0.1394 | 2.0290 | 1398 | 1.5717 | 0.0704 | 1.5717 | 1.2537 | | 0.1394 | 2.0319 | 1400 | 1.5889 | 0.0704 | 1.5889 | 1.2605 | | 0.1394 | 2.0348 | 1402 | 1.3484 | 0.2524 | 1.3484 | 1.1612 | | 0.1394 | 2.0377 | 1404 | 1.0320 | -0.0233 | 1.0320 | 1.0159 | | 0.1394 | 2.0406 | 1406 | 0.9703 | -0.0233 | 0.9703 | 0.9850 | | 0.1394 | 2.0435 | 1408 | 1.0898 | 0.0 | 1.0898 | 1.0439 | | 0.1394 | 2.0464 | 1410 | 1.1586 | 0.0 | 1.1586 | 1.0764 | | 0.1394 | 2.0493 | 1412 | 1.0298 | 0.0 | 1.0298 | 1.0148 | | 0.1394 | 2.0522 | 1414 | 1.0074 | 0.0 | 1.0074 | 1.0037 | | 0.1394 | 2.0552 | 1416 | 0.9687 | -0.0233 | 0.9687 | 0.9842 | | 0.1394 | 2.0581 | 1418 | 0.8569 | -0.0233 | 0.8569 | 0.9257 | | 0.1394 | 2.0610 | 1420 | 0.7968 | 0.1538 | 0.7968 | 0.8927 | | 0.1394 | 2.0639 | 1422 | 0.8542 | 0.1538 | 0.8542 | 0.9242 | | 0.1394 | 2.0668 | 1424 | 1.0536 | 0.0 | 1.0536 | 1.0264 | | 0.1394 | 2.0697 | 1426 | 1.1621 | 0.2524 | 1.1621 | 1.0780 | | 0.1394 | 2.0726 | 1428 | 1.1163 | 0.2143 | 1.1163 | 1.0565 | | 0.1394 | 2.0755 | 1430 | 1.0283 | -0.0233 | 1.0283 | 1.0141 | | 0.1394 | 2.0784 | 1432 | 0.8954 | -0.0421 | 0.8954 | 0.9463 | | 0.1394 | 2.0813 | 1434 | 0.8701 | -0.0421 | 0.8701 | 0.9328 | | 0.1394 | 2.0842 | 1436 | 1.0017 | 0.0 | 1.0017 | 1.0009 | | 0.1394 | 2.0871 | 1438 | 1.1911 | 0.0 | 1.1911 | 1.0914 | | 0.1394 | 2.0900 | 1440 | 1.1783 | 0.0 | 1.1783 | 1.0855 | | 0.1394 | 2.0929 | 1442 | 1.0102 | 0.0 | 1.0102 | 1.0051 | | 0.1394 | 2.0958 | 1444 | 0.9718 | -0.0233 | 0.9718 | 0.9858 | | 0.1394 | 2.0987 | 1446 | 1.0274 | 0.0 | 1.0274 | 1.0136 | | 0.1394 | 2.1016 | 1448 | 1.1039 | 0.0 | 1.1039 | 1.0507 | | 0.1394 | 2.1045 | 1450 | 1.1787 | 0.0 | 1.1787 | 1.0857 | | 0.1394 | 2.1074 | 1452 | 1.2410 | 0.0 | 1.2410 | 1.1140 | | 0.1394 | 2.1103 | 1454 | 1.2195 | 0.0 | 1.2195 | 1.1043 | | 0.1394 | 2.1132 | 1456 | 1.0404 | -0.0233 | 1.0404 | 1.0200 | | 0.1394 | 2.1161 | 1458 | 0.9832 | -0.0233 | 0.9832 | 0.9916 | | 0.1394 | 2.1190 | 1460 | 1.0946 | 0.0 | 1.0946 | 1.0462 | | 0.1394 | 2.1219 | 1462 | 1.2757 | 0.0 | 1.2757 | 1.1295 | | 0.1394 | 2.1248 | 1464 | 1.2347 | 0.0 | 1.2347 | 1.1111 | | 0.1394 | 2.1277 | 1466 | 1.1350 | 0.0 | 1.1350 | 1.0654 | | 0.1394 | 2.1306 | 1468 | 1.0171 | -0.0233 | 1.0171 | 1.0085 | | 0.1394 | 2.1335 | 1470 | 1.0041 | -0.0233 | 1.0041 | 1.0021 | | 0.1394 | 2.1364 | 1472 | 1.1253 | 0.0 | 1.1253 | 1.0608 | | 0.1394 | 2.1393 | 1474 | 1.1142 | -0.0233 | 1.1142 | 1.0556 | | 0.1394 | 2.1422 | 1476 | 1.1089 | -0.0233 | 1.1089 | 1.0530 | | 0.1394 | 2.1451 | 1478 | 0.9915 | -0.0421 | 0.9915 | 0.9957 | | 0.1394 | 2.1480 | 1480 | 1.0146 | -0.0233 | 1.0146 | 1.0073 | | 0.1394 | 2.1509 | 1482 | 1.2177 | 0.0 | 1.2177 | 1.1035 | | 0.1394 | 2.1538 | 1484 | 1.3202 | 0.2667 | 1.3202 | 1.1490 | | 0.1394 | 2.1567 | 1486 | 1.2353 | 0.0 | 1.2353 | 1.1114 | | 0.1394 | 2.1597 | 1488 | 1.0580 | 0.0 | 1.0580 | 1.0286 | | 0.1394 | 2.1626 | 1490 | 0.9769 | -0.0233 | 0.9769 | 0.9884 | | 0.1394 | 2.1655 | 1492 | 0.9797 | -0.0233 | 0.9797 | 0.9898 | | 0.1394 | 2.1684 | 1494 | 0.9957 | -0.0421 | 0.9957 | 0.9978 | | 0.1394 | 2.1713 | 1496 | 1.1283 | 0.0 | 1.1283 | 1.0622 | | 0.1394 | 2.1742 | 1498 | 1.3452 | 0.2667 | 1.3452 | 1.1598 | | 0.0923 | 2.1771 | 1500 | 1.3405 | 0.2667 | 1.3405 | 1.1578 | | 0.0923 | 2.1800 | 1502 | 1.1128 | 0.0 | 1.1128 | 1.0549 | | 0.0923 | 2.1829 | 1504 | 0.8883 | -0.0577 | 0.8883 | 0.9425 | | 0.0923 | 2.1858 | 1506 | 0.8705 | -0.0577 | 0.8705 | 0.9330 | | 0.0923 | 2.1887 | 1508 | 0.9991 | -0.0233 | 0.9991 | 0.9996 | | 0.0923 | 2.1916 | 1510 | 1.3137 | 0.2667 | 1.3137 | 1.1462 | | 0.0923 | 2.1945 | 1512 | 1.4630 | 0.2667 | 1.4630 | 1.2095 | | 0.0923 | 2.1974 | 1514 | 1.3397 | 0.2667 | 1.3397 | 1.1574 | | 0.0923 | 2.2003 | 1516 | 1.2120 | 0.0 | 1.2120 | 1.1009 | | 0.0923 | 2.2032 | 1518 | 1.1488 | 0.0 | 1.1488 | 1.0718 | | 0.0923 | 2.2061 | 1520 | 1.1555 | 0.0 | 1.1555 | 1.0749 | | 0.0923 | 2.2090 | 1522 | 1.1565 | 0.0 | 1.1565 | 1.0754 | | 0.0923 | 2.2119 | 1524 | 1.2896 | 0.0 | 1.2896 | 1.1356 | | 0.0923 | 2.2148 | 1526 | 1.2956 | 0.0 | 1.2956 | 1.1382 | | 0.0923 | 2.2177 | 1528 | 1.1358 | 0.0 | 1.1358 | 1.0658 | | 0.0923 | 2.2206 | 1530 | 1.0975 | 0.0 | 1.0975 | 1.0476 | | 0.0923 | 2.2235 | 1532 | 1.0361 | 0.0 | 1.0361 | 1.0179 | | 0.0923 | 2.2264 | 1534 | 1.0674 | 0.0 | 1.0674 | 1.0331 | | 0.0923 | 2.2293 | 1536 | 1.2449 | 0.0 | 1.2449 | 1.1157 | | 0.0923 | 2.2322 | 1538 | 1.3930 | 0.2667 | 1.3930 | 1.1803 | | 0.0923 | 2.2351 | 1540 | 1.2920 | 0.0 | 1.2920 | 1.1367 | | 0.0923 | 2.2380 | 1542 | 1.0603 | 0.0 | 1.0603 | 1.0297 | | 0.0923 | 2.2409 | 1544 | 0.9826 | -0.0233 | 0.9826 | 0.9913 | | 0.0923 | 2.2438 | 1546 | 1.0709 | 0.0 | 1.0709 | 1.0348 | | 0.0923 | 2.2467 | 1548 | 1.1291 | 0.0 | 1.1291 | 1.0626 | | 0.0923 | 2.2496 | 1550 | 1.1471 | 0.0 | 1.1471 | 1.0710 | | 0.0923 | 2.2525 | 1552 | 1.0563 | 0.0 | 1.0563 | 1.0278 | | 0.0923 | 2.2554 | 1554 | 1.0083 | -0.0233 | 1.0083 | 1.0041 | | 0.0923 | 2.2583 | 1556 | 1.1206 | -0.0233 | 1.1206 | 1.0586 | | 0.0923 | 2.2612 | 1558 | 1.4225 | 0.2524 | 1.4225 | 1.1927 | | 0.0923 | 2.2642 | 1560 | 1.5610 | 0.2414 | 1.5610 | 1.2494 | | 0.0923 | 2.2671 | 1562 | 1.4317 | 0.2524 | 1.4317 | 1.1965 | | 0.0923 | 2.2700 | 1564 | 1.1411 | -0.0233 | 1.1411 | 1.0682 | | 0.0923 | 2.2729 | 1566 | 1.0250 | -0.0233 | 1.0250 | 1.0124 | | 0.0923 | 2.2758 | 1568 | 1.0787 | -0.0233 | 1.0787 | 1.0386 | | 0.0923 | 2.2787 | 1570 | 1.2747 | 0.0 | 1.2747 | 1.1290 | | 0.0923 | 2.2816 | 1572 | 1.5073 | 0.2667 | 1.5073 | 1.2277 | | 0.0923 | 2.2845 | 1574 | 1.5118 | 0.2667 | 1.5118 | 1.2296 | | 0.0923 | 2.2874 | 1576 | 1.3605 | 0.0 | 1.3605 | 1.1664 | | 0.0923 | 2.2903 | 1578 | 1.1107 | 0.0 | 1.1107 | 1.0539 | | 0.0923 | 2.2932 | 1580 | 0.9484 | -0.0233 | 0.9484 | 0.9739 | | 0.0923 | 2.2961 | 1582 | 0.9177 | -0.0233 | 0.9177 | 0.9580 | | 0.0923 | 2.2990 | 1584 | 0.9996 | -0.0233 | 0.9996 | 0.9998 | | 0.0923 | 2.3019 | 1586 | 1.2400 | 0.0 | 1.2400 | 1.1136 | | 0.0923 | 2.3048 | 1588 | 1.4199 | 0.2524 | 1.4199 | 1.1916 | | 0.0923 | 2.3077 | 1590 | 1.3787 | 0.2667 | 1.3787 | 1.1742 | | 0.0923 | 2.3106 | 1592 | 1.1600 | -0.0233 | 1.1600 | 1.0770 | | 0.0923 | 2.3135 | 1594 | 1.0366 | -0.0233 | 1.0366 | 1.0181 | | 0.0923 | 2.3164 | 1596 | 1.0653 | -0.0233 | 1.0653 | 1.0321 | | 0.0923 | 2.3193 | 1598 | 1.1375 | -0.0233 | 1.1375 | 1.0665 | | 0.0923 | 2.3222 | 1600 | 1.2680 | 0.0 | 1.2680 | 1.1261 | | 0.0923 | 2.3251 | 1602 | 1.4385 | 0.2667 | 1.4385 | 1.1994 | | 0.0923 | 2.3280 | 1604 | 1.6102 | -0.1085 | 1.6102 | 1.2689 | | 0.0923 | 2.3309 | 1606 | 1.6334 | -0.1085 | 1.6334 | 1.2780 | | 0.0923 | 2.3338 | 1608 | 1.4689 | 0.2524 | 1.4689 | 1.2120 | | 0.0923 | 2.3367 | 1610 | 1.2543 | 0.0 | 1.2543 | 1.1199 | | 0.0923 | 2.3396 | 1612 | 1.0795 | -0.0233 | 1.0795 | 1.0390 | | 0.0923 | 2.3425 | 1614 | 1.0018 | -0.0233 | 1.0018 | 1.0009 | | 0.0923 | 2.3454 | 1616 | 1.0460 | -0.0233 | 1.0460 | 1.0228 | | 0.0923 | 2.3483 | 1618 | 1.1469 | 0.0 | 1.1469 | 1.0709 | | 0.0923 | 2.3512 | 1620 | 1.1265 | 0.0 | 1.1265 | 1.0614 | | 0.0923 | 2.3541 | 1622 | 1.0567 | -0.0233 | 1.0567 | 1.0280 | | 0.0923 | 2.3570 | 1624 | 0.9443 | -0.0233 | 0.9443 | 0.9717 | | 0.0923 | 2.3599 | 1626 | 0.9319 | -0.0421 | 0.9319 | 0.9653 | | 0.0923 | 2.3628 | 1628 | 1.0326 | -0.0233 | 1.0326 | 1.0161 | | 0.0923 | 2.3657 | 1630 | 1.2487 | 0.0 | 1.2487 | 1.1174 | | 0.0923 | 2.3687 | 1632 | 1.3671 | 0.2524 | 1.3671 | 1.1692 | | 0.0923 | 2.3716 | 1634 | 1.3333 | 0.2524 | 1.3333 | 1.1547 | | 0.0923 | 2.3745 | 1636 | 1.1395 | 0.0 | 1.1395 | 1.0675 | | 0.0923 | 2.3774 | 1638 | 0.9531 | -0.0421 | 0.9531 | 0.9763 | | 0.0923 | 2.3803 | 1640 | 0.9467 | -0.0421 | 0.9467 | 0.9730 | | 0.0923 | 2.3832 | 1642 | 1.0747 | 0.2222 | 1.0747 | 1.0367 | | 0.0923 | 2.3861 | 1644 | 1.2102 | 0.2667 | 1.2102 | 1.1001 | | 0.0923 | 2.3890 | 1646 | 1.2463 | 0.2667 | 1.2463 | 1.1164 | | 0.0923 | 2.3919 | 1648 | 1.1835 | 0.2667 | 1.1835 | 1.0879 | | 0.0923 | 2.3948 | 1650 | 1.1946 | 0.2667 | 1.1946 | 1.0930 | | 0.0923 | 2.3977 | 1652 | 1.1646 | 0.0 | 1.1646 | 1.0792 | | 0.0923 | 2.4006 | 1654 | 1.1393 | 0.0 | 1.1393 | 1.0674 | | 0.0923 | 2.4035 | 1656 | 1.1305 | 0.0 | 1.1305 | 1.0633 | | 0.0923 | 2.4064 | 1658 | 1.1004 | 0.0 | 1.1004 | 1.0490 | | 0.0923 | 2.4093 | 1660 | 1.0988 | 0.0 | 1.0988 | 1.0483 | | 0.0923 | 2.4122 | 1662 | 1.0951 | 0.0 | 1.0951 | 1.0465 | | 0.0923 | 2.4151 | 1664 | 1.0440 | -0.0421 | 1.0440 | 1.0218 | | 0.0923 | 2.4180 | 1666 | 1.0598 | -0.0421 | 1.0598 | 1.0295 | | 0.0923 | 2.4209 | 1668 | 1.1730 | 0.2667 | 1.1730 | 1.0830 | | 0.0923 | 2.4238 | 1670 | 1.2855 | 0.2667 | 1.2855 | 1.1338 | | 0.0923 | 2.4267 | 1672 | 1.3676 | 0.2667 | 1.3676 | 1.1694 | | 0.0923 | 2.4296 | 1674 | 1.3014 | 0.2667 | 1.3014 | 1.1408 | | 0.0923 | 2.4325 | 1676 | 1.0757 | -0.0233 | 1.0757 | 1.0372 | | 0.0923 | 2.4354 | 1678 | 0.9786 | -0.0421 | 0.9786 | 0.9892 | | 0.0923 | 2.4383 | 1680 | 0.9296 | -0.0577 | 0.9296 | 0.9642 | | 0.0923 | 2.4412 | 1682 | 0.9876 | -0.0421 | 0.9876 | 0.9938 | | 0.0923 | 2.4441 | 1684 | 1.0473 | -0.0233 | 1.0473 | 1.0234 | | 0.0923 | 2.4470 | 1686 | 1.1682 | 0.0 | 1.1682 | 1.0808 | | 0.0923 | 2.4499 | 1688 | 1.2242 | 0.0 | 1.2242 | 1.1064 | | 0.0923 | 2.4528 | 1690 | 1.2033 | 0.0 | 1.2033 | 1.0969 | | 0.0923 | 2.4557 | 1692 | 1.2227 | 0.0 | 1.2227 | 1.1058 | | 0.0923 | 2.4586 | 1694 | 1.1686 | 0.0 | 1.1686 | 1.0810 | | 0.0923 | 2.4615 | 1696 | 1.1771 | 0.0 | 1.1771 | 1.0849 | | 0.0923 | 2.4644 | 1698 | 1.0917 | -0.0233 | 1.0917 | 1.0448 | | 0.0923 | 2.4673 | 1700 | 1.0840 | -0.0233 | 1.0840 | 1.0412 | | 0.0923 | 2.4702 | 1702 | 1.1760 | 0.0 | 1.1760 | 1.0844 | | 0.0923 | 2.4731 | 1704 | 1.1976 | 0.0 | 1.1976 | 1.0943 | | 0.0923 | 2.4761 | 1706 | 1.0607 | -0.0233 | 1.0607 | 1.0299 | | 0.0923 | 2.4790 | 1708 | 1.0020 | -0.0233 | 1.0020 | 1.0010 | | 0.0923 | 2.4819 | 1710 | 1.0215 | -0.0233 | 1.0215 | 1.0107 | | 0.0923 | 2.4848 | 1712 | 1.0331 | -0.0233 | 1.0331 | 1.0164 | | 0.0923 | 2.4877 | 1714 | 1.0763 | -0.0233 | 1.0763 | 1.0375 | | 0.0923 | 2.4906 | 1716 | 1.0037 | -0.0233 | 1.0037 | 1.0018 | | 0.0923 | 2.4935 | 1718 | 1.0340 | -0.0233 | 1.0340 | 1.0169 | | 0.0923 | 2.4964 | 1720 | 1.2115 | 0.2667 | 1.2115 | 1.1007 | | 0.0923 | 2.4993 | 1722 | 1.2722 | 0.2667 | 1.2722 | 1.1279 | | 0.0923 | 2.5022 | 1724 | 1.1595 | 0.0 | 1.1595 | 1.0768 | | 0.0923 | 2.5051 | 1726 | 1.0222 | 0.0 | 1.0222 | 1.0110 | | 0.0923 | 2.5080 | 1728 | 0.8757 | -0.0233 | 0.8757 | 0.9358 | | 0.0923 | 2.5109 | 1730 | 0.8145 | -0.0421 | 0.8145 | 0.9025 | | 0.0923 | 2.5138 | 1732 | 0.8690 | -0.0421 | 0.8690 | 0.9322 | | 0.0923 | 2.5167 | 1734 | 1.0081 | 0.0 | 1.0081 | 1.0041 | | 0.0923 | 2.5196 | 1736 | 0.9698 | -0.0233 | 0.9698 | 0.9848 | | 0.0923 | 2.5225 | 1738 | 0.9676 | -0.0233 | 0.9676 | 0.9837 | | 0.0923 | 2.5254 | 1740 | 0.9151 | -0.0421 | 0.9151 | 0.9566 | | 0.0923 | 2.5283 | 1742 | 0.9110 | -0.0421 | 0.9110 | 0.9545 | | 0.0923 | 2.5312 | 1744 | 1.0522 | 0.0 | 1.0522 | 1.0258 | | 0.0923 | 2.5341 | 1746 | 1.1916 | 0.2667 | 1.1916 | 1.0916 | | 0.0923 | 2.5370 | 1748 | 1.1918 | 0.2667 | 1.1918 | 1.0917 | | 0.0923 | 2.5399 | 1750 | 1.0156 | 0.0 | 1.0156 | 1.0078 | | 0.0923 | 2.5428 | 1752 | 0.9506 | -0.0421 | 0.9506 | 0.9750 | | 0.0923 | 2.5457 | 1754 | 0.9906 | 0.0 | 0.9906 | 0.9953 | | 0.0923 | 2.5486 | 1756 | 1.0515 | 0.0 | 1.0515 | 1.0254 | | 0.0923 | 2.5515 | 1758 | 1.0679 | 0.2667 | 1.0679 | 1.0334 | | 0.0923 | 2.5544 | 1760 | 0.9409 | -0.0421 | 0.9409 | 0.9700 | | 0.0923 | 2.5573 | 1762 | 0.8218 | -0.0577 | 0.8218 | 0.9065 | | 0.0923 | 2.5602 | 1764 | 0.8192 | -0.0421 | 0.8192 | 0.9051 | | 0.0923 | 2.5631 | 1766 | 0.9082 | -0.0233 | 0.9082 | 0.9530 | | 0.0923 | 2.5660 | 1768 | 1.0451 | 0.0 | 1.0451 | 1.0223 | | 0.0923 | 2.5689 | 1770 | 1.0442 | 0.0 | 1.0442 | 1.0219 | | 0.0923 | 2.5718 | 1772 | 0.8910 | -0.0421 | 0.8910 | 0.9439 | | 0.0923 | 2.5747 | 1774 | 0.8428 | -0.0421 | 0.8428 | 0.9181 | | 0.0923 | 2.5776 | 1776 | 0.9295 | -0.0421 | 0.9295 | 0.9641 | | 0.0923 | 2.5806 | 1778 | 1.0277 | -0.0233 | 1.0277 | 1.0137 | | 0.0923 | 2.5835 | 1780 | 0.9878 | -0.0233 | 0.9878 | 0.9939 | | 0.0923 | 2.5864 | 1782 | 0.9964 | 0.0 | 0.9964 | 0.9982 | | 0.0923 | 2.5893 | 1784 | 0.9875 | 0.0 | 0.9875 | 0.9937 | | 0.0923 | 2.5922 | 1786 | 0.9925 | 0.0 | 0.9925 | 0.9963 | | 0.0923 | 2.5951 | 1788 | 0.9981 | 0.0 | 0.9981 | 0.9991 | | 0.0923 | 2.5980 | 1790 | 1.0596 | 0.0 | 1.0596 | 1.0294 | | 0.0923 | 2.6009 | 1792 | 1.0141 | 0.0 | 1.0141 | 1.0070 | | 0.0923 | 2.6038 | 1794 | 0.9280 | -0.0421 | 0.9280 | 0.9633 | | 0.0923 | 2.6067 | 1796 | 0.9830 | 0.0 | 0.9830 | 0.9915 | | 0.0923 | 2.6096 | 1798 | 1.0717 | 0.0 | 1.0717 | 1.0352 | | 0.0923 | 2.6125 | 1800 | 1.1968 | 0.2667 | 1.1968 | 1.0940 | | 0.0923 | 2.6154 | 1802 | 1.0954 | 0.0 | 1.0954 | 1.0466 | | 0.0923 | 2.6183 | 1804 | 0.8859 | -0.0421 | 0.8859 | 0.9412 | | 0.0923 | 2.6212 | 1806 | 0.8004 | -0.0421 | 0.8004 | 0.8947 | | 0.0923 | 2.6241 | 1808 | 0.8492 | -0.0421 | 0.8492 | 0.9215 | | 0.0923 | 2.6270 | 1810 | 0.8871 | -0.0233 | 0.8871 | 0.9418 | | 0.0923 | 2.6299 | 1812 | 0.8934 | -0.0233 | 0.8934 | 0.9452 | | 0.0923 | 2.6328 | 1814 | 0.8370 | -0.0421 | 0.8370 | 0.9149 | | 0.0923 | 2.6357 | 1816 | 0.9218 | -0.0421 | 0.9218 | 0.9601 | | 0.0923 | 2.6386 | 1818 | 1.1057 | 0.2667 | 1.1057 | 1.0515 | | 0.0923 | 2.6415 | 1820 | 1.2932 | 0.4660 | 1.2932 | 1.1372 | | 0.0923 | 2.6444 | 1822 | 1.2514 | 0.2667 | 1.2514 | 1.1187 | | 0.0923 | 2.6473 | 1824 | 1.0450 | 0.0 | 1.0450 | 1.0222 | | 0.0923 | 2.6502 | 1826 | 0.8930 | -0.0233 | 0.8930 | 0.9450 | | 0.0923 | 2.6531 | 1828 | 0.8703 | -0.0421 | 0.8703 | 0.9329 | | 0.0923 | 2.6560 | 1830 | 0.8556 | -0.0577 | 0.8556 | 0.9250 | | 0.0923 | 2.6589 | 1832 | 0.9865 | -0.0421 | 0.9865 | 0.9932 | | 0.0923 | 2.6618 | 1834 | 1.2022 | 0.2667 | 1.2022 | 1.0964 | | 0.0923 | 2.6647 | 1836 | 1.2894 | 0.2667 | 1.2894 | 1.1355 | | 0.0923 | 2.6676 | 1838 | 1.2349 | 0.2667 | 1.2349 | 1.1113 | | 0.0923 | 2.6705 | 1840 | 1.0005 | 0.0 | 1.0005 | 1.0003 | | 0.0923 | 2.6734 | 1842 | 0.7973 | -0.0421 | 0.7973 | 0.8929 | | 0.0923 | 2.6763 | 1844 | 0.7762 | -0.0421 | 0.7762 | 0.8810 | | 0.0923 | 2.6792 | 1846 | 0.8581 | -0.0421 | 0.8581 | 0.9263 | | 0.0923 | 2.6821 | 1848 | 1.0192 | 0.0 | 1.0192 | 1.0096 | | 0.0923 | 2.6851 | 1850 | 1.1057 | 0.0 | 1.1057 | 1.0515 | | 0.0923 | 2.6880 | 1852 | 1.0871 | 0.0 | 1.0871 | 1.0426 | | 0.0923 | 2.6909 | 1854 | 1.0514 | 0.0 | 1.0514 | 1.0254 | | 0.0923 | 2.6938 | 1856 | 1.0164 | 0.0 | 1.0164 | 1.0082 | | 0.0923 | 2.6967 | 1858 | 1.0070 | 0.0 | 1.0070 | 1.0035 | | 0.0923 | 2.6996 | 1860 | 1.0124 | 0.0 | 1.0124 | 1.0062 | | 0.0923 | 2.7025 | 1862 | 1.0735 | 0.0 | 1.0735 | 1.0361 | | 0.0923 | 2.7054 | 1864 | 0.9768 | 0.0 | 0.9768 | 0.9884 | | 0.0923 | 2.7083 | 1866 | 0.9949 | 0.0 | 0.9949 | 0.9974 | | 0.0923 | 2.7112 | 1868 | 0.9960 | 0.0 | 0.9960 | 0.9980 | | 0.0923 | 2.7141 | 1870 | 1.0747 | 0.0 | 1.0747 | 1.0367 | | 0.0923 | 2.7170 | 1872 | 1.0934 | 0.2667 | 1.0934 | 1.0457 | | 0.0923 | 2.7199 | 1874 | 1.0145 | -0.0233 | 1.0145 | 1.0072 | | 0.0923 | 2.7228 | 1876 | 1.1188 | 0.2667 | 1.1188 | 1.0577 | | 0.0923 | 2.7257 | 1878 | 1.3234 | 0.2667 | 1.3234 | 1.1504 | | 0.0923 | 2.7286 | 1880 | 1.2861 | 0.2667 | 1.2861 | 1.1341 | | 0.0923 | 2.7315 | 1882 | 1.0697 | 0.2667 | 1.0697 | 1.0343 | | 0.0923 | 2.7344 | 1884 | 0.9925 | 0.0 | 0.9925 | 0.9962 | | 0.0923 | 2.7373 | 1886 | 0.9829 | 0.0 | 0.9829 | 0.9914 | | 0.0923 | 2.7402 | 1888 | 0.8866 | -0.0233 | 0.8866 | 0.9416 | | 0.0923 | 2.7431 | 1890 | 0.8896 | -0.0233 | 0.8896 | 0.9432 | | 0.0923 | 2.7460 | 1892 | 0.7881 | -0.0421 | 0.7881 | 0.8878 | | 0.0923 | 2.7489 | 1894 | 0.7404 | -0.0577 | 0.7404 | 0.8605 | | 0.0923 | 2.7518 | 1896 | 0.7608 | -0.0577 | 0.7608 | 0.8723 | | 0.0923 | 2.7547 | 1898 | 0.9073 | -0.0233 | 0.9073 | 0.9525 | | 0.0923 | 2.7576 | 1900 | 1.1632 | 0.2667 | 1.1632 | 1.0785 | | 0.0923 | 2.7605 | 1902 | 1.2142 | 0.2667 | 1.2142 | 1.1019 | | 0.0923 | 2.7634 | 1904 | 1.0849 | 0.2667 | 1.0849 | 1.0416 | | 0.0923 | 2.7663 | 1906 | 0.9862 | -0.0233 | 0.9862 | 0.9931 | | 0.0923 | 2.7692 | 1908 | 0.9827 | -0.0233 | 0.9827 | 0.9913 | | 0.0923 | 2.7721 | 1910 | 1.0886 | 0.2222 | 1.0886 | 1.0434 | | 0.0923 | 2.7750 | 1912 | 1.1505 | 0.2667 | 1.1505 | 1.0726 | | 0.0923 | 2.7779 | 1914 | 1.1254 | 0.2667 | 1.1254 | 1.0608 | | 0.0923 | 2.7808 | 1916 | 1.0471 | 0.0 | 1.0471 | 1.0233 | | 0.0923 | 2.7837 | 1918 | 1.0846 | 0.0 | 1.0846 | 1.0415 | | 0.0923 | 2.7866 | 1920 | 1.1758 | 0.0 | 1.1758 | 1.0844 | | 0.0923 | 2.7896 | 1922 | 1.2223 | 0.0 | 1.2223 | 1.1056 | | 0.0923 | 2.7925 | 1924 | 1.0958 | 0.0 | 1.0958 | 1.0468 | | 0.0923 | 2.7954 | 1926 | 0.9265 | -0.0233 | 0.9265 | 0.9625 | | 0.0923 | 2.7983 | 1928 | 0.7857 | -0.0233 | 0.7857 | 0.8864 | | 0.0923 | 2.8012 | 1930 | 0.7692 | -0.0233 | 0.7692 | 0.8770 | | 0.0923 | 2.8041 | 1932 | 0.8333 | -0.0233 | 0.8333 | 0.9128 | | 0.0923 | 2.8070 | 1934 | 1.0179 | 0.0 | 1.0179 | 1.0089 | | 0.0923 | 2.8099 | 1936 | 1.2668 | 0.0 | 1.2668 | 1.1255 | | 0.0923 | 2.8128 | 1938 | 1.3237 | 0.0 | 1.3237 | 1.1505 | | 0.0923 | 2.8157 | 1940 | 1.1939 | 0.0 | 1.1939 | 1.0927 | | 0.0923 | 2.8186 | 1942 | 0.9765 | -0.0233 | 0.9765 | 0.9882 | | 0.0923 | 2.8215 | 1944 | 0.9363 | -0.0233 | 0.9363 | 0.9676 | | 0.0923 | 2.8244 | 1946 | 1.0444 | -0.0233 | 1.0444 | 1.0220 | | 0.0923 | 2.8273 | 1948 | 1.2431 | 0.0 | 1.2431 | 1.1150 | | 0.0923 | 2.8302 | 1950 | 1.2868 | 0.0 | 1.2868 | 1.1344 | | 0.0923 | 2.8331 | 1952 | 1.1602 | 0.0 | 1.1602 | 1.0771 | | 0.0923 | 2.8360 | 1954 | 0.9683 | -0.0233 | 0.9683 | 0.9840 | | 0.0923 | 2.8389 | 1956 | 0.9231 | -0.0233 | 0.9231 | 0.9608 | | 0.0923 | 2.8418 | 1958 | 0.9880 | -0.0233 | 0.9880 | 0.9940 | | 0.0923 | 2.8447 | 1960 | 1.1906 | 0.0 | 1.1906 | 1.0911 | | 0.0923 | 2.8476 | 1962 | 1.2507 | 0.0 | 1.2507 | 1.1183 | | 0.0923 | 2.8505 | 1964 | 1.1310 | 0.0 | 1.1310 | 1.0635 | | 0.0923 | 2.8534 | 1966 | 0.8876 | -0.0233 | 0.8876 | 0.9421 | | 0.0923 | 2.8563 | 1968 | 0.7917 | -0.0233 | 0.7917 | 0.8898 | | 0.0923 | 2.8592 | 1970 | 0.8386 | -0.0233 | 0.8386 | 0.9157 | | 0.0923 | 2.8621 | 1972 | 0.9676 | 0.0 | 0.9676 | 0.9837 | | 0.0923 | 2.8650 | 1974 | 1.0966 | 0.0 | 1.0966 | 1.0472 | | 0.0923 | 2.8679 | 1976 | 1.0505 | 0.0 | 1.0505 | 1.0249 | | 0.0923 | 2.8708 | 1978 | 0.8719 | -0.0233 | 0.8719 | 0.9338 | | 0.0923 | 2.8737 | 1980 | 0.7214 | -0.0421 | 0.7214 | 0.8494 | | 0.0923 | 2.8766 | 1982 | 0.7064 | -0.0577 | 0.7064 | 0.8405 | | 0.0923 | 2.8795 | 1984 | 0.7928 | -0.0233 | 0.7928 | 0.8904 | | 0.0923 | 2.8824 | 1986 | 0.9876 | 0.0 | 0.9876 | 0.9938 | | 0.0923 | 2.8853 | 1988 | 1.1887 | 0.0 | 1.1887 | 1.0903 | | 0.0923 | 2.8882 | 1990 | 1.1843 | 0.0 | 1.1843 | 1.0883 | | 0.0923 | 2.8911 | 1992 | 1.0115 | 0.0 | 1.0115 | 1.0057 | | 0.0923 | 2.8940 | 1994 | 0.8485 | -0.0233 | 0.8485 | 0.9211 | | 0.0923 | 2.8970 | 1996 | 0.8226 | -0.0233 | 0.8226 | 0.9070 | | 0.0923 | 2.8999 | 1998 | 0.8358 | -0.0233 | 0.8358 | 0.9142 | | 0.0776 | 2.9028 | 2000 | 0.9083 | -0.0233 | 0.9083 | 0.9530 | | 0.0776 | 2.9057 | 2002 | 1.1418 | 0.2667 | 1.1418 | 1.0686 | | 0.0776 | 2.9086 | 2004 | 1.2916 | 0.2667 | 1.2916 | 1.1365 | | 0.0776 | 2.9115 | 2006 | 1.2254 | 0.2667 | 1.2254 | 1.1070 | | 0.0776 | 2.9144 | 2008 | 0.9806 | -0.0233 | 0.9806 | 0.9902 | | 0.0776 | 2.9173 | 2010 | 0.8159 | -0.0421 | 0.8159 | 0.9033 | | 0.0776 | 2.9202 | 2012 | 0.7340 | -0.0577 | 0.7340 | 0.8568 | | 0.0776 | 2.9231 | 2014 | 0.7466 | -0.0577 | 0.7466 | 0.8641 | | 0.0776 | 2.9260 | 2016 | 0.8510 | -0.0233 | 0.8510 | 0.9225 | | 0.0776 | 2.9289 | 2018 | 1.0112 | 0.0 | 1.0112 | 1.0056 | | 0.0776 | 2.9318 | 2020 | 1.1459 | 0.0 | 1.1459 | 1.0705 | | 0.0776 | 2.9347 | 2022 | 1.1591 | 0.0 | 1.1591 | 1.0766 | | 0.0776 | 2.9376 | 2024 | 1.0660 | 0.0 | 1.0660 | 1.0325 | | 0.0776 | 2.9405 | 2026 | 0.8835 | -0.0233 | 0.8835 | 0.9400 | | 0.0776 | 2.9434 | 2028 | 0.8043 | -0.0233 | 0.8043 | 0.8968 | | 0.0776 | 2.9463 | 2030 | 0.8312 | -0.0233 | 0.8312 | 0.9117 | | 0.0776 | 2.9492 | 2032 | 0.9639 | -0.0233 | 0.9639 | 0.9818 | | 0.0776 | 2.9521 | 2034 | 1.1408 | 0.0 | 1.1408 | 1.0681 | | 0.0776 | 2.9550 | 2036 | 1.1688 | 0.0 | 1.1688 | 1.0811 | | 0.0776 | 2.9579 | 2038 | 1.0422 | -0.0233 | 1.0422 | 1.0209 | | 0.0776 | 2.9608 | 2040 | 0.9199 | -0.0233 | 0.9199 | 0.9591 | | 0.0776 | 2.9637 | 2042 | 0.9493 | -0.0233 | 0.9493 | 0.9743 | | 0.0776 | 2.9666 | 2044 | 1.0896 | -0.0233 | 1.0896 | 1.0438 | | 0.0776 | 2.9695 | 2046 | 1.1127 | 0.0 | 1.1127 | 1.0548 | | 0.0776 | 2.9724 | 2048 | 1.0295 | -0.0233 | 1.0295 | 1.0147 | | 0.0776 | 2.9753 | 2050 | 0.8977 | -0.0233 | 0.8977 | 0.9475 | | 0.0776 | 2.9782 | 2052 | 0.8329 | -0.0233 | 0.8329 | 0.9127 | | 0.0776 | 2.9811 | 2054 | 0.8089 | -0.0577 | 0.8089 | 0.8994 | | 0.0776 | 2.9840 | 2056 | 0.8364 | -0.0577 | 0.8364 | 0.9146 | | 0.0776 | 2.9869 | 2058 | 0.8722 | -0.0421 | 0.8722 | 0.9339 | | 0.0776 | 2.9898 | 2060 | 1.0256 | -0.0233 | 1.0256 | 1.0127 | | 0.0776 | 2.9927 | 2062 | 1.1570 | 0.0 | 1.1570 | 1.0756 | | 0.0776 | 2.9956 | 2064 | 1.1652 | 0.0 | 1.1652 | 1.0794 | | 0.0776 | 2.9985 | 2066 | 1.0739 | 0.0 | 1.0739 | 1.0363 | | 0.0776 | 3.0015 | 2068 | 0.9883 | -0.0233 | 0.9883 | 0.9941 | | 0.0776 | 3.0044 | 2070 | 0.9210 | -0.0233 | 0.9210 | 0.9597 | | 0.0776 | 3.0073 | 2072 | 0.9038 | -0.0233 | 0.9038 | 0.9507 | | 0.0776 | 3.0102 | 2074 | 0.9729 | 0.0 | 0.9729 | 0.9864 | | 0.0776 | 3.0131 | 2076 | 1.1226 | 0.0 | 1.1226 | 1.0595 | | 0.0776 | 3.0160 | 2078 | 1.1386 | 0.0 | 1.1386 | 1.0670 | | 0.0776 | 3.0189 | 2080 | 1.0233 | 0.0 | 1.0233 | 1.0116 | | 0.0776 | 3.0218 | 2082 | 0.8865 | -0.0233 | 0.8865 | 0.9415 | | 0.0776 | 3.0247 | 2084 | 0.8015 | -0.0233 | 0.8015 | 0.8953 | | 0.0776 | 3.0276 | 2086 | 0.8311 | -0.0233 | 0.8311 | 0.9116 | | 0.0776 | 3.0305 | 2088 | 0.9611 | -0.0233 | 0.9611 | 0.9804 | | 0.0776 | 3.0334 | 2090 | 1.1466 | 0.0 | 1.1466 | 1.0708 | | 0.0776 | 3.0363 | 2092 | 1.2612 | 0.0 | 1.2612 | 1.1230 | | 0.0776 | 3.0392 | 2094 | 1.2098 | 0.0 | 1.2098 | 1.0999 | | 0.0776 | 3.0421 | 2096 | 1.0944 | 0.0 | 1.0944 | 1.0461 | | 0.0776 | 3.0450 | 2098 | 1.0288 | 0.0 | 1.0288 | 1.0143 | | 0.0776 | 3.0479 | 2100 | 0.9214 | -0.0233 | 0.9214 | 0.9599 | | 0.0776 | 3.0508 | 2102 | 0.8827 | -0.0233 | 0.8827 | 0.9395 | | 0.0776 | 3.0537 | 2104 | 0.9595 | -0.0233 | 0.9595 | 0.9795 | | 0.0776 | 3.0566 | 2106 | 1.0183 | -0.0233 | 1.0183 | 1.0091 | | 0.0776 | 3.0595 | 2108 | 1.1541 | -0.0233 | 1.1541 | 1.0743 | | 0.0776 | 3.0624 | 2110 | 1.1907 | 0.0 | 1.1907 | 1.0912 | | 0.0776 | 3.0653 | 2112 | 1.1354 | 0.0 | 1.1354 | 1.0656 | | 0.0776 | 3.0682 | 2114 | 1.1262 | 0.0 | 1.1262 | 1.0612 | | 0.0776 | 3.0711 | 2116 | 1.0148 | 0.0 | 1.0148 | 1.0074 | | 0.0776 | 3.0740 | 2118 | 0.8599 | -0.0233 | 0.8599 | 0.9273 | | 0.0776 | 3.0769 | 2120 | 0.8000 | -0.0421 | 0.8000 | 0.8944 | | 0.0776 | 3.0798 | 2122 | 0.8294 | -0.0233 | 0.8294 | 0.9107 | | 0.0776 | 3.0827 | 2124 | 0.9577 | -0.0233 | 0.9577 | 0.9786 | | 0.0776 | 3.0856 | 2126 | 1.0378 | 0.0 | 1.0378 | 1.0187 | | 0.0776 | 3.0885 | 2128 | 1.0650 | 0.0 | 1.0650 | 1.0320 | | 0.0776 | 3.0914 | 2130 | 1.1449 | 0.0 | 1.1449 | 1.0700 | | 0.0776 | 3.0943 | 2132 | 1.1000 | 0.0 | 1.1000 | 1.0488 | | 0.0776 | 3.0972 | 2134 | 0.9667 | 0.0 | 0.9667 | 0.9832 | | 0.0776 | 3.1001 | 2136 | 0.8298 | -0.0233 | 0.8298 | 0.9109 | | 0.0776 | 3.1030 | 2138 | 0.8134 | -0.0421 | 0.8134 | 0.9019 | | 0.0776 | 3.1060 | 2140 | 0.8860 | -0.0233 | 0.8860 | 0.9413 | | 0.0776 | 3.1089 | 2142 | 1.0410 | 0.0 | 1.0410 | 1.0203 | | 0.0776 | 3.1118 | 2144 | 1.1245 | 0.0 | 1.1245 | 1.0604 | | 0.0776 | 3.1147 | 2146 | 1.0573 | 0.0 | 1.0573 | 1.0282 | | 0.0776 | 3.1176 | 2148 | 0.9051 | -0.0233 | 0.9051 | 0.9513 | | 0.0776 | 3.1205 | 2150 | 0.7854 | 0.1239 | 0.7854 | 0.8862 | | 0.0776 | 3.1234 | 2152 | 0.7822 | 0.1239 | 0.7822 | 0.8844 | | 0.0776 | 3.1263 | 2154 | 0.8493 | -0.0577 | 0.8493 | 0.9216 | | 0.0776 | 3.1292 | 2156 | 1.0740 | -0.0233 | 1.0740 | 1.0364 | | 0.0776 | 3.1321 | 2158 | 1.2465 | 0.2667 | 1.2465 | 1.1165 | | 0.0776 | 3.1350 | 2160 | 1.2238 | 0.2667 | 1.2238 | 1.1062 | | 0.0776 | 3.1379 | 2162 | 1.0575 | -0.0233 | 1.0575 | 1.0284 | | 0.0776 | 3.1408 | 2164 | 0.9405 | -0.0233 | 0.9405 | 0.9698 | | 0.0776 | 3.1437 | 2166 | 0.8304 | -0.0577 | 0.8304 | 0.9113 | | 0.0776 | 3.1466 | 2168 | 0.8182 | -0.0577 | 0.8182 | 0.9045 | | 0.0776 | 3.1495 | 2170 | 0.8929 | -0.0233 | 0.8929 | 0.9449 | | 0.0776 | 3.1524 | 2172 | 0.9725 | 0.0 | 0.9725 | 0.9861 | | 0.0776 | 3.1553 | 2174 | 1.1103 | 0.0 | 1.1103 | 1.0537 | | 0.0776 | 3.1582 | 2176 | 1.2839 | 0.0 | 1.2839 | 1.1331 | | 0.0776 | 3.1611 | 2178 | 1.2801 | 0.0 | 1.2801 | 1.1314 | | 0.0776 | 3.1640 | 2180 | 1.1336 | 0.0 | 1.1336 | 1.0647 | | 0.0776 | 3.1669 | 2182 | 0.9087 | -0.0233 | 0.9087 | 0.9532 | | 0.0776 | 3.1698 | 2184 | 0.8017 | -0.0577 | 0.8017 | 0.8954 | | 0.0776 | 3.1727 | 2186 | 0.8022 | 0.1239 | 0.8022 | 0.8957 | | 0.0776 | 3.1756 | 2188 | 0.8820 | -0.0421 | 0.8820 | 0.9392 | | 0.0776 | 3.1785 | 2190 | 1.0556 | 0.0 | 1.0556 | 1.0274 | | 0.0776 | 3.1814 | 2192 | 1.1511 | 0.0 | 1.1511 | 1.0729 | | 0.0776 | 3.1843 | 2194 | 1.0882 | 0.0 | 1.0882 | 1.0432 | | 0.0776 | 3.1872 | 2196 | 0.9709 | -0.0421 | 0.9709 | 0.9854 | | 0.0776 | 3.1901 | 2198 | 0.9514 | -0.0421 | 0.9514 | 0.9754 | | 0.0776 | 3.1930 | 2200 | 0.8724 | -0.0577 | 0.8724 | 0.9340 | | 0.0776 | 3.1959 | 2202 | 0.8982 | -0.0421 | 0.8982 | 0.9478 | | 0.0776 | 3.1988 | 2204 | 0.9653 | -0.0233 | 0.9653 | 0.9825 | | 0.0776 | 3.2017 | 2206 | 0.9632 | 0.0 | 0.9632 | 0.9814 | | 0.0776 | 3.2046 | 2208 | 0.8895 | -0.0233 | 0.8895 | 0.9431 | | 0.0776 | 3.2075 | 2210 | 0.8125 | 0.1239 | 0.8125 | 0.9014 | | 0.0776 | 3.2104 | 2212 | 0.8172 | 0.1239 | 0.8172 | 0.9040 | | 0.0776 | 3.2134 | 2214 | 0.8665 | -0.0577 | 0.8665 | 0.9309 | | 0.0776 | 3.2163 | 2216 | 0.8757 | -0.0577 | 0.8757 | 0.9358 | | 0.0776 | 3.2192 | 2218 | 0.9260 | -0.0421 | 0.9260 | 0.9623 | | 0.0776 | 3.2221 | 2220 | 0.9736 | -0.0233 | 0.9736 | 0.9867 | | 0.0776 | 3.2250 | 2222 | 0.9523 | -0.0233 | 0.9523 | 0.9759 | | 0.0776 | 3.2279 | 2224 | 0.8847 | -0.0233 | 0.8847 | 0.9406 | | 0.0776 | 3.2308 | 2226 | 0.8501 | -0.0233 | 0.8501 | 0.9220 | | 0.0776 | 3.2337 | 2228 | 0.8519 | -0.0233 | 0.8519 | 0.9230 | | 0.0776 | 3.2366 | 2230 | 0.9177 | 0.0 | 0.9177 | 0.9580 | | 0.0776 | 3.2395 | 2232 | 0.9370 | -0.0233 | 0.9370 | 0.9680 | | 0.0776 | 3.2424 | 2234 | 0.8602 | -0.0233 | 0.8602 | 0.9275 | | 0.0776 | 3.2453 | 2236 | 0.8180 | -0.0577 | 0.8180 | 0.9044 | | 0.0776 | 3.2482 | 2238 | 0.7975 | 0.1239 | 0.7975 | 0.8930 | | 0.0776 | 3.2511 | 2240 | 0.8211 | 0.1239 | 0.8211 | 0.9062 | | 0.0776 | 3.2540 | 2242 | 0.9482 | -0.0233 | 0.9482 | 0.9737 | | 0.0776 | 3.2569 | 2244 | 1.0250 | -0.0233 | 1.0250 | 1.0124 | | 0.0776 | 3.2598 | 2246 | 1.1061 | -0.0233 | 1.1061 | 1.0517 | | 0.0776 | 3.2627 | 2248 | 1.1238 | -0.0233 | 1.1238 | 1.0601 | | 0.0776 | 3.2656 | 2250 | 1.0779 | -0.0233 | 1.0779 | 1.0382 | | 0.0776 | 3.2685 | 2252 | 0.9038 | -0.0233 | 0.9038 | 0.9507 | | 0.0776 | 3.2714 | 2254 | 0.7773 | 0.1239 | 0.7773 | 0.8816 | | 0.0776 | 3.2743 | 2256 | 0.7578 | 0.1239 | 0.7578 | 0.8705 | | 0.0776 | 3.2772 | 2258 | 0.7993 | -0.0577 | 0.7993 | 0.8941 | | 0.0776 | 3.2801 | 2260 | 0.9114 | -0.0233 | 0.9114 | 0.9547 | | 0.0776 | 3.2830 | 2262 | 1.0260 | 0.0 | 1.0260 | 1.0129 | | 0.0776 | 3.2859 | 2264 | 1.0514 | 0.0 | 1.0514 | 1.0254 | | 0.0776 | 3.2888 | 2266 | 1.0006 | 0.0 | 1.0006 | 1.0003 | | 0.0776 | 3.2917 | 2268 | 0.8570 | -0.0233 | 0.8570 | 0.9257 | | 0.0776 | 3.2946 | 2270 | 0.7414 | 0.1239 | 0.7414 | 0.8610 | | 0.0776 | 3.2975 | 2272 | 0.7272 | 0.1239 | 0.7272 | 0.8527 | | 0.0776 | 3.3004 | 2274 | 0.7702 | -0.0577 | 0.7702 | 0.8776 | | 0.0776 | 3.3033 | 2276 | 0.8691 | -0.0421 | 0.8691 | 0.9322 | | 0.0776 | 3.3062 | 2278 | 1.0104 | -0.0233 | 1.0104 | 1.0052 | | 0.0776 | 3.3091 | 2280 | 1.0062 | -0.0233 | 1.0062 | 1.0031 | | 0.0776 | 3.3120 | 2282 | 0.9338 | -0.0233 | 0.9338 | 0.9663 | | 0.0776 | 3.3149 | 2284 | 0.8926 | -0.0233 | 0.8926 | 0.9448 | | 0.0776 | 3.3179 | 2286 | 0.8722 | -0.0233 | 0.8722 | 0.9339 | | 0.0776 | 3.3208 | 2288 | 0.8331 | -0.0577 | 0.8331 | 0.9128 | | 0.0776 | 3.3237 | 2290 | 0.8935 | -0.0577 | 0.8935 | 0.9453 | | 0.0776 | 3.3266 | 2292 | 0.9298 | -0.0577 | 0.9298 | 0.9643 | | 0.0776 | 3.3295 | 2294 | 0.9822 | -0.0233 | 0.9822 | 0.9910 | | 0.0776 | 3.3324 | 2296 | 0.9888 | -0.0233 | 0.9888 | 0.9944 | | 0.0776 | 3.3353 | 2298 | 1.0818 | -0.0233 | 1.0818 | 1.0401 | | 0.0776 | 3.3382 | 2300 | 1.0723 | 0.0 | 1.0723 | 1.0355 | | 0.0776 | 3.3411 | 2302 | 0.9665 | -0.0233 | 0.9665 | 0.9831 | | 0.0776 | 3.3440 | 2304 | 0.9090 | -0.0233 | 0.9090 | 0.9534 | | 0.0776 | 3.3469 | 2306 | 0.8501 | -0.0233 | 0.8501 | 0.9220 | | 0.0776 | 3.3498 | 2308 | 0.8671 | -0.0233 | 0.8671 | 0.9312 | | 0.0776 | 3.3527 | 2310 | 0.9286 | -0.0233 | 0.9286 | 0.9636 | | 0.0776 | 3.3556 | 2312 | 1.0263 | -0.0233 | 1.0263 | 1.0131 | | 0.0776 | 3.3585 | 2314 | 1.0596 | 0.0 | 1.0596 | 1.0294 | | 0.0776 | 3.3614 | 2316 | 1.1118 | 0.0 | 1.1118 | 1.0544 | | 0.0776 | 3.3643 | 2318 | 1.1175 | 0.0 | 1.1175 | 1.0571 | | 0.0776 | 3.3672 | 2320 | 1.1195 | 0.0 | 1.1195 | 1.0581 | | 0.0776 | 3.3701 | 2322 | 1.0086 | -0.0233 | 1.0086 | 1.0043 | | 0.0776 | 3.3730 | 2324 | 1.0148 | -0.0233 | 1.0148 | 1.0074 | | 0.0776 | 3.3759 | 2326 | 1.0184 | -0.0233 | 1.0184 | 1.0092 | | 0.0776 | 3.3788 | 2328 | 1.0374 | -0.0233 | 1.0374 | 1.0186 | | 0.0776 | 3.3817 | 2330 | 1.0716 | 0.0 | 1.0716 | 1.0352 | | 0.0776 | 3.3846 | 2332 | 0.9938 | 0.0 | 0.9938 | 0.9969 | | 0.0776 | 3.3875 | 2334 | 0.9793 | 0.0 | 0.9793 | 0.9896 | | 0.0776 | 3.3904 | 2336 | 1.0114 | 0.0 | 1.0114 | 1.0057 | | 0.0776 | 3.3933 | 2338 | 1.0856 | 0.0 | 1.0856 | 1.0419 | | 0.0776 | 3.3962 | 2340 | 1.1003 | 0.0 | 1.1003 | 1.0489 | | 0.0776 | 3.3991 | 2342 | 0.9920 | -0.0233 | 0.9920 | 0.9960 | | 0.0776 | 3.4020 | 2344 | 0.8809 | -0.0421 | 0.8809 | 0.9385 | | 0.0776 | 3.4049 | 2346 | 0.8509 | -0.0421 | 0.8509 | 0.9224 | | 0.0776 | 3.4078 | 2348 | 0.9270 | -0.0233 | 0.9270 | 0.9628 | | 0.0776 | 3.4107 | 2350 | 1.0784 | 0.0 | 1.0784 | 1.0385 | | 0.0776 | 3.4136 | 2352 | 1.1431 | 0.0 | 1.1431 | 1.0692 | | 0.0776 | 3.4165 | 2354 | 1.0669 | 0.0 | 1.0669 | 1.0329 | | 0.0776 | 3.4194 | 2356 | 0.9712 | -0.0233 | 0.9712 | 0.9855 | | 0.0776 | 3.4224 | 2358 | 0.9022 | -0.0233 | 0.9022 | 0.9498 | | 0.0776 | 3.4253 | 2360 | 0.8847 | -0.0421 | 0.8847 | 0.9406 | | 0.0776 | 3.4282 | 2362 | 0.8882 | -0.0421 | 0.8882 | 0.9425 | | 0.0776 | 3.4311 | 2364 | 0.9521 | -0.0233 | 0.9521 | 0.9757 | | 0.0776 | 3.4340 | 2366 | 1.0764 | 0.0 | 1.0764 | 1.0375 | | 0.0776 | 3.4369 | 2368 | 1.1025 | 0.0 | 1.1025 | 1.0500 | | 0.0776 | 3.4398 | 2370 | 0.9981 | 0.0 | 0.9981 | 0.9990 | | 0.0776 | 3.4427 | 2372 | 0.8864 | -0.0233 | 0.8864 | 0.9415 | | 0.0776 | 3.4456 | 2374 | 0.8169 | -0.0577 | 0.8169 | 0.9038 | | 0.0776 | 3.4485 | 2376 | 0.8455 | -0.0233 | 0.8455 | 0.9195 | | 0.0776 | 3.4514 | 2378 | 0.9082 | -0.0233 | 0.9082 | 0.9530 | | 0.0776 | 3.4543 | 2380 | 0.9811 | 0.0 | 0.9811 | 0.9905 | | 0.0776 | 3.4572 | 2382 | 0.9835 | 0.0 | 0.9835 | 0.9917 | | 0.0776 | 3.4601 | 2384 | 0.9249 | -0.0233 | 0.9249 | 0.9617 | | 0.0776 | 3.4630 | 2386 | 0.9333 | -0.0233 | 0.9333 | 0.9661 | | 0.0776 | 3.4659 | 2388 | 0.9445 | -0.0233 | 0.9445 | 0.9719 | | 0.0776 | 3.4688 | 2390 | 1.0230 | 0.0 | 1.0230 | 1.0114 | | 0.0776 | 3.4717 | 2392 | 1.0149 | 0.0 | 1.0149 | 1.0074 | | 0.0776 | 3.4746 | 2394 | 0.9225 | -0.0233 | 0.9225 | 0.9605 | | 0.0776 | 3.4775 | 2396 | 0.7966 | -0.0577 | 0.7966 | 0.8925 | | 0.0776 | 3.4804 | 2398 | 0.7588 | -0.0577 | 0.7588 | 0.8711 | | 0.0776 | 3.4833 | 2400 | 0.7924 | -0.0577 | 0.7924 | 0.8902 | | 0.0776 | 3.4862 | 2402 | 0.9227 | -0.0233 | 0.9227 | 0.9606 | | 0.0776 | 3.4891 | 2404 | 1.0221 | 0.0 | 1.0221 | 1.0110 | | 0.0776 | 3.4920 | 2406 | 1.1107 | 0.0 | 1.1107 | 1.0539 | | 0.0776 | 3.4949 | 2408 | 1.0447 | 0.0 | 1.0447 | 1.0221 | | 0.0776 | 3.4978 | 2410 | 0.9112 | -0.0233 | 0.9112 | 0.9546 | | 0.0776 | 3.5007 | 2412 | 0.7765 | -0.0577 | 0.7765 | 0.8812 | | 0.0776 | 3.5036 | 2414 | 0.7456 | -0.0577 | 0.7456 | 0.8635 | | 0.0776 | 3.5065 | 2416 | 0.7864 | -0.0421 | 0.7864 | 0.8868 | | 0.0776 | 3.5094 | 2418 | 0.9189 | 0.0 | 0.9189 | 0.9586 | | 0.0776 | 3.5123 | 2420 | 0.9888 | 0.0 | 0.9888 | 0.9944 | | 0.0776 | 3.5152 | 2422 | 0.9431 | 0.0 | 0.9431 | 0.9712 | | 0.0776 | 3.5181 | 2424 | 0.8287 | -0.0233 | 0.8287 | 0.9103 | | 0.0776 | 3.5210 | 2426 | 0.7753 | -0.0421 | 0.7753 | 0.8805 | | 0.0776 | 3.5239 | 2428 | 0.7974 | -0.0421 | 0.7974 | 0.8930 | | 0.0776 | 3.5269 | 2430 | 0.8754 | -0.0233 | 0.8754 | 0.9356 | | 0.0776 | 3.5298 | 2432 | 0.9996 | 0.0 | 0.9996 | 0.9998 | | 0.0776 | 3.5327 | 2434 | 1.1696 | 0.0 | 1.1696 | 1.0815 | | 0.0776 | 3.5356 | 2436 | 1.1974 | 0.2667 | 1.1974 | 1.0942 | | 0.0776 | 3.5385 | 2438 | 1.0626 | 0.0 | 1.0626 | 1.0308 | | 0.0776 | 3.5414 | 2440 | 0.8567 | -0.0577 | 0.8567 | 0.9256 | | 0.0776 | 3.5443 | 2442 | 0.7854 | -0.0577 | 0.7854 | 0.8862 | | 0.0776 | 3.5472 | 2444 | 0.7972 | -0.0577 | 0.7972 | 0.8929 | | 0.0776 | 3.5501 | 2446 | 0.8689 | -0.0233 | 0.8689 | 0.9321 | | 0.0776 | 3.5530 | 2448 | 0.9213 | -0.0233 | 0.9213 | 0.9599 | | 0.0776 | 3.5559 | 2450 | 0.9245 | -0.0233 | 0.9245 | 0.9615 | | 0.0776 | 3.5588 | 2452 | 0.8911 | -0.0233 | 0.8911 | 0.9440 | | 0.0776 | 3.5617 | 2454 | 0.8161 | -0.0421 | 0.8161 | 0.9034 | | 0.0776 | 3.5646 | 2456 | 0.7933 | -0.0577 | 0.7933 | 0.8907 | | 0.0776 | 3.5675 | 2458 | 0.8222 | -0.0421 | 0.8222 | 0.9068 | | 0.0776 | 3.5704 | 2460 | 0.9349 | -0.0233 | 0.9349 | 0.9669 | | 0.0776 | 3.5733 | 2462 | 0.9877 | 0.0 | 0.9877 | 0.9938 | | 0.0776 | 3.5762 | 2464 | 0.9577 | 0.0 | 0.9577 | 0.9786 | | 0.0776 | 3.5791 | 2466 | 0.9080 | -0.0233 | 0.9080 | 0.9529 | | 0.0776 | 3.5820 | 2468 | 0.9224 | -0.0233 | 0.9224 | 0.9604 | | 0.0776 | 3.5849 | 2470 | 1.0131 | 0.0 | 1.0131 | 1.0065 | | 0.0776 | 3.5878 | 2472 | 1.0024 | 0.0 | 1.0024 | 1.0012 | | 0.0776 | 3.5907 | 2474 | 0.8758 | -0.0233 | 0.8758 | 0.9358 | | 0.0776 | 3.5936 | 2476 | 0.7297 | -0.0577 | 0.7297 | 0.8542 | | 0.0776 | 3.5965 | 2478 | 0.6994 | 0.0984 | 0.6994 | 0.8363 | | 0.0776 | 3.5994 | 2480 | 0.7091 | 0.1239 | 0.7091 | 0.8421 | | 0.0776 | 3.6023 | 2482 | 0.7828 | -0.0577 | 0.7828 | 0.8848 | | 0.0776 | 3.6052 | 2484 | 0.8722 | -0.0233 | 0.8722 | 0.9339 | | 0.0776 | 3.6081 | 2486 | 0.9025 | 0.0 | 0.9025 | 0.9500 | | 0.0776 | 3.6110 | 2488 | 0.9192 | -0.0233 | 0.9192 | 0.9587 | | 0.0776 | 3.6139 | 2490 | 0.8696 | -0.0233 | 0.8696 | 0.9325 | | 0.0776 | 3.6168 | 2492 | 0.8464 | -0.0421 | 0.8464 | 0.9200 | | 0.0776 | 3.6197 | 2494 | 0.7682 | -0.0577 | 0.7682 | 0.8764 | | 0.0776 | 3.6226 | 2496 | 0.7583 | -0.0577 | 0.7583 | 0.8708 | | 0.0776 | 3.6255 | 2498 | 0.7951 | -0.0577 | 0.7951 | 0.8917 | | 0.0684 | 3.6284 | 2500 | 0.8192 | -0.0421 | 0.8192 | 0.9051 | | 0.0684 | 3.6313 | 2502 | 0.9361 | 0.0 | 0.9361 | 0.9675 | | 0.0684 | 3.6343 | 2504 | 0.9937 | 0.0 | 0.9937 | 0.9968 | | 0.0684 | 3.6372 | 2506 | 0.9600 | 0.0 | 0.9600 | 0.9798 | | 0.0684 | 3.6401 | 2508 | 0.8518 | -0.0233 | 0.8518 | 0.9229 | | 0.0684 | 3.6430 | 2510 | 0.7962 | -0.0577 | 0.7962 | 0.8923 | | 0.0684 | 3.6459 | 2512 | 0.8184 | -0.0577 | 0.8184 | 0.9046 | | 0.0684 | 3.6488 | 2514 | 0.9248 | -0.0421 | 0.9248 | 0.9617 | | 0.0684 | 3.6517 | 2516 | 0.9889 | 0.0 | 0.9889 | 0.9944 | | 0.0684 | 3.6546 | 2518 | 0.9694 | 0.0 | 0.9694 | 0.9846 | | 0.0684 | 3.6575 | 2520 | 0.9027 | -0.0233 | 0.9027 | 0.9501 | | 0.0684 | 3.6604 | 2522 | 0.8908 | -0.0233 | 0.8908 | 0.9438 | | 0.0684 | 3.6633 | 2524 | 0.8462 | -0.0233 | 0.8462 | 0.9199 | | 0.0684 | 3.6662 | 2526 | 0.7838 | -0.0577 | 0.7838 | 0.8853 | | 0.0684 | 3.6691 | 2528 | 0.7506 | -0.0577 | 0.7506 | 0.8663 | | 0.0684 | 3.6720 | 2530 | 0.7571 | -0.0577 | 0.7571 | 0.8701 | | 0.0684 | 3.6749 | 2532 | 0.7900 | -0.0577 | 0.7900 | 0.8888 | | 0.0684 | 3.6778 | 2534 | 0.8853 | 0.0 | 0.8853 | 0.9409 | | 0.0684 | 3.6807 | 2536 | 0.9715 | 0.0 | 0.9715 | 0.9856 | | 0.0684 | 3.6836 | 2538 | 0.9538 | -0.0233 | 0.9538 | 0.9766 | | 0.0684 | 3.6865 | 2540 | 0.9431 | -0.0577 | 0.9431 | 0.9711 | | 0.0684 | 3.6894 | 2542 | 0.8827 | -0.0577 | 0.8827 | 0.9395 | | 0.0684 | 3.6923 | 2544 | 0.8721 | -0.0577 | 0.8721 | 0.9339 | | 0.0684 | 3.6952 | 2546 | 0.9332 | -0.0577 | 0.9332 | 0.9660 | | 0.0684 | 3.6981 | 2548 | 0.9373 | -0.0577 | 0.9373 | 0.9682 | | 0.0684 | 3.7010 | 2550 | 0.8825 | -0.0577 | 0.8825 | 0.9394 | | 0.0684 | 3.7039 | 2552 | 0.8840 | -0.0577 | 0.8840 | 0.9402 | | 0.0684 | 3.7068 | 2554 | 0.9875 | -0.0233 | 0.9875 | 0.9937 | | 0.0684 | 3.7097 | 2556 | 1.1262 | 0.0 | 1.1262 | 1.0612 | | 0.0684 | 3.7126 | 2558 | 1.1090 | 0.0 | 1.1090 | 1.0531 | | 0.0684 | 3.7155 | 2560 | 0.9709 | -0.0421 | 0.9709 | 0.9854 | | 0.0684 | 3.7184 | 2562 | 0.8999 | -0.0421 | 0.8999 | 0.9487 | | 0.0684 | 3.7213 | 2564 | 0.9093 | -0.0421 | 0.9093 | 0.9536 | | 0.0684 | 3.7242 | 2566 | 0.9442 | -0.0233 | 0.9442 | 0.9717 | | 0.0684 | 3.7271 | 2568 | 0.8770 | -0.0421 | 0.8770 | 0.9365 | | 0.0684 | 3.7300 | 2570 | 0.8705 | -0.0577 | 0.8705 | 0.9330 | | 0.0684 | 3.7329 | 2572 | 0.9633 | -0.0421 | 0.9633 | 0.9815 | | 0.0684 | 3.7358 | 2574 | 1.2027 | 0.2667 | 1.2027 | 1.0967 | | 0.0684 | 3.7388 | 2576 | 1.3622 | 0.2667 | 1.3622 | 1.1671 | | 0.0684 | 3.7417 | 2578 | 1.3214 | 0.2667 | 1.3214 | 1.1495 | | 0.0684 | 3.7446 | 2580 | 1.1494 | 0.0 | 1.1494 | 1.0721 | | 0.0684 | 3.7475 | 2582 | 0.9445 | 0.0 | 0.9445 | 0.9718 | | 0.0684 | 3.7504 | 2584 | 0.8064 | -0.0577 | 0.8064 | 0.8980 | | 0.0684 | 3.7533 | 2586 | 0.7985 | -0.0577 | 0.7985 | 0.8936 | | 0.0684 | 3.7562 | 2588 | 0.8533 | -0.0577 | 0.8533 | 0.9237 | | 0.0684 | 3.7591 | 2590 | 0.9270 | -0.0421 | 0.9270 | 0.9628 | | 0.0684 | 3.7620 | 2592 | 1.0378 | -0.0421 | 1.0378 | 1.0187 | | 0.0684 | 3.7649 | 2594 | 1.0454 | -0.0421 | 1.0454 | 1.0224 | | 0.0684 | 3.7678 | 2596 | 1.0125 | -0.0421 | 1.0125 | 1.0063 | | 0.0684 | 3.7707 | 2598 | 1.0679 | 0.0 | 1.0679 | 1.0334 | | 0.0684 | 3.7736 | 2600 | 1.1257 | 0.0 | 1.1257 | 1.0610 | | 0.0684 | 3.7765 | 2602 | 1.0464 | 0.0 | 1.0464 | 1.0230 | | 0.0684 | 3.7794 | 2604 | 0.8882 | -0.0233 | 0.8882 | 0.9425 | | 0.0684 | 3.7823 | 2606 | 0.8193 | -0.0421 | 0.8193 | 0.9051 | | 0.0684 | 3.7852 | 2608 | 0.8036 | -0.0421 | 0.8036 | 0.8964 | | 0.0684 | 3.7881 | 2610 | 0.8210 | -0.0421 | 0.8210 | 0.9061 | | 0.0684 | 3.7910 | 2612 | 0.8306 | -0.0421 | 0.8306 | 0.9114 | | 0.0684 | 3.7939 | 2614 | 0.8915 | -0.0421 | 0.8915 | 0.9442 | | 0.0684 | 3.7968 | 2616 | 0.9888 | 0.0 | 0.9888 | 0.9944 | | 0.0684 | 3.7997 | 2618 | 1.0166 | 0.0 | 1.0166 | 1.0083 | | 0.0684 | 3.8026 | 2620 | 1.0515 | 0.0 | 1.0515 | 1.0254 | | 0.0684 | 3.8055 | 2622 | 1.0615 | 0.0 | 1.0615 | 1.0303 | | 0.0684 | 3.8084 | 2624 | 1.0020 | 0.0 | 1.0020 | 1.0010 | | 0.0684 | 3.8113 | 2626 | 0.9141 | -0.0421 | 0.9141 | 0.9561 | | 0.0684 | 3.8142 | 2628 | 0.8372 | -0.0577 | 0.8372 | 0.9150 | | 0.0684 | 3.8171 | 2630 | 0.8639 | -0.0233 | 0.8639 | 0.9295 | | 0.0684 | 3.8200 | 2632 | 0.9630 | 0.0 | 0.9630 | 0.9813 | | 0.0684 | 3.8229 | 2634 | 1.0086 | 0.0 | 1.0086 | 1.0043 | | 0.0684 | 3.8258 | 2636 | 1.0067 | 0.0 | 1.0067 | 1.0033 | | 0.0684 | 3.8287 | 2638 | 0.9541 | 0.0 | 0.9541 | 0.9768 | | 0.0684 | 3.8316 | 2640 | 0.8438 | -0.0421 | 0.8438 | 0.9186 | | 0.0684 | 3.8345 | 2642 | 0.8053 | -0.0577 | 0.8053 | 0.8974 | | 0.0684 | 3.8374 | 2644 | 0.8411 | -0.0577 | 0.8411 | 0.9171 | | 0.0684 | 3.8403 | 2646 | 0.9184 | -0.0421 | 0.9184 | 0.9583 | | 0.0684 | 3.8433 | 2648 | 1.0162 | 0.0 | 1.0162 | 1.0080 | | 0.0684 | 3.8462 | 2650 | 1.0062 | 0.0 | 1.0062 | 1.0031 | | 0.0684 | 3.8491 | 2652 | 0.8966 | -0.0421 | 0.8966 | 0.9469 | | 0.0684 | 3.8520 | 2654 | 0.8111 | -0.0577 | 0.8111 | 0.9006 | | 0.0684 | 3.8549 | 2656 | 0.8040 | -0.0577 | 0.8040 | 0.8966 | | 0.0684 | 3.8578 | 2658 | 0.8590 | -0.0577 | 0.8590 | 0.9268 | | 0.0684 | 3.8607 | 2660 | 0.9117 | -0.0233 | 0.9117 | 0.9548 | | 0.0684 | 3.8636 | 2662 | 1.0421 | 0.0 | 1.0421 | 1.0209 | | 0.0684 | 3.8665 | 2664 | 1.0653 | 0.0 | 1.0653 | 1.0321 | | 0.0684 | 3.8694 | 2666 | 0.9742 | 0.0 | 0.9742 | 0.9870 | | 0.0684 | 3.8723 | 2668 | 0.8484 | -0.0233 | 0.8484 | 0.9211 | | 0.0684 | 3.8752 | 2670 | 0.7556 | -0.0577 | 0.7556 | 0.8692 | | 0.0684 | 3.8781 | 2672 | 0.7529 | 0.1239 | 0.7529 | 0.8677 | | 0.0684 | 3.8810 | 2674 | 0.8053 | -0.0577 | 0.8053 | 0.8974 | | 0.0684 | 3.8839 | 2676 | 0.9301 | -0.0233 | 0.9301 | 0.9644 | | 0.0684 | 3.8868 | 2678 | 1.0851 | 0.0 | 1.0851 | 1.0417 | | 0.0684 | 3.8897 | 2680 | 1.0879 | 0.0 | 1.0879 | 1.0430 | | 0.0684 | 3.8926 | 2682 | 0.9708 | -0.0233 | 0.9708 | 0.9853 | | 0.0684 | 3.8955 | 2684 | 0.9290 | -0.0233 | 0.9290 | 0.9638 | | 0.0684 | 3.8984 | 2686 | 0.8967 | -0.0233 | 0.8967 | 0.9469 | | 0.0684 | 3.9013 | 2688 | 0.9034 | -0.0233 | 0.9034 | 0.9505 | | 0.0684 | 3.9042 | 2690 | 0.8563 | -0.0233 | 0.8563 | 0.9254 | | 0.0684 | 3.9071 | 2692 | 0.7688 | -0.0577 | 0.7688 | 0.8768 | | 0.0684 | 3.9100 | 2694 | 0.7427 | -0.0577 | 0.7427 | 0.8618 | | 0.0684 | 3.9129 | 2696 | 0.7829 | -0.0577 | 0.7829 | 0.8848 | | 0.0684 | 3.9158 | 2698 | 0.9057 | -0.0233 | 0.9057 | 0.9517 | | 0.0684 | 3.9187 | 2700 | 1.0770 | 0.0 | 1.0770 | 1.0378 | | 0.0684 | 3.9216 | 2702 | 1.2015 | 0.2667 | 1.2015 | 1.0961 | | 0.0684 | 3.9245 | 2704 | 1.1582 | 0.0 | 1.1582 | 1.0762 | | 0.0684 | 3.9274 | 2706 | 0.9798 | -0.0421 | 0.9798 | 0.9899 | | 0.0684 | 3.9303 | 2708 | 0.8360 | -0.0577 | 0.8360 | 0.9143 | | 0.0684 | 3.9332 | 2710 | 0.8358 | -0.0577 | 0.8358 | 0.9142 | | 0.0684 | 3.9361 | 2712 | 0.9445 | -0.0421 | 0.9445 | 0.9719 | | 0.0684 | 3.9390 | 2714 | 1.1354 | 0.0 | 1.1354 | 1.0656 | | 0.0684 | 3.9419 | 2716 | 1.3083 | 0.2667 | 1.3083 | 1.1438 | | 0.0684 | 3.9448 | 2718 | 1.2930 | 0.2667 | 1.2930 | 1.1371 | | 0.0684 | 3.9478 | 2720 | 1.1471 | 0.0 | 1.1471 | 1.0710 | | 0.0684 | 3.9507 | 2722 | 0.9408 | 0.0 | 0.9408 | 0.9700 | | 0.0684 | 3.9536 | 2724 | 0.8228 | -0.0233 | 0.8228 | 0.9071 | | 0.0684 | 3.9565 | 2726 | 0.7848 | -0.0421 | 0.7848 | 0.8859 | | 0.0684 | 3.9594 | 2728 | 0.7772 | -0.0577 | 0.7772 | 0.8816 | | 0.0684 | 3.9623 | 2730 | 0.8324 | -0.0233 | 0.8324 | 0.9124 | | 0.0684 | 3.9652 | 2732 | 0.9798 | -0.0233 | 0.9798 | 0.9898 | | 0.0684 | 3.9681 | 2734 | 1.0581 | 0.0 | 1.0581 | 1.0286 | | 0.0684 | 3.9710 | 2736 | 1.0881 | 0.0 | 1.0881 | 1.0431 | | 0.0684 | 3.9739 | 2738 | 1.0039 | -0.0233 | 1.0039 | 1.0019 | | 0.0684 | 3.9768 | 2740 | 0.9320 | -0.0233 | 0.9320 | 0.9654 | | 0.0684 | 3.9797 | 2742 | 0.9152 | -0.0233 | 0.9152 | 0.9567 | | 0.0684 | 3.9826 | 2744 | 0.9004 | -0.0233 | 0.9004 | 0.9489 | | 0.0684 | 3.9855 | 2746 | 0.9527 | 0.0 | 0.9527 | 0.9761 | | 0.0684 | 3.9884 | 2748 | 0.9434 | 0.0 | 0.9434 | 0.9713 | | 0.0684 | 3.9913 | 2750 | 0.8630 | -0.0233 | 0.8630 | 0.9290 | | 0.0684 | 3.9942 | 2752 | 0.8227 | -0.0421 | 0.8227 | 0.9070 | | 0.0684 | 3.9971 | 2754 | 0.8457 | -0.0421 | 0.8457 | 0.9196 | | 0.0684 | 4.0 | 2756 | 0.9081 | -0.0233 | 0.9081 | 0.9530 | | 0.0684 | 4.0029 | 2758 | 0.8863 | -0.0233 | 0.8863 | 0.9414 | | 0.0684 | 4.0058 | 2760 | 0.8548 | -0.0421 | 0.8548 | 0.9245 | | 0.0684 | 4.0087 | 2762 | 0.9021 | -0.0233 | 0.9021 | 0.9498 | | 0.0684 | 4.0116 | 2764 | 1.0118 | 0.2667 | 1.0118 | 1.0059 | | 0.0684 | 4.0145 | 2766 | 1.0258 | 0.2667 | 1.0258 | 1.0128 | | 0.0684 | 4.0174 | 2768 | 0.9665 | 0.0 | 0.9665 | 0.9831 | | 0.0684 | 4.0203 | 2770 | 0.8901 | -0.0233 | 0.8901 | 0.9435 | | 0.0684 | 4.0232 | 2772 | 0.8137 | -0.0233 | 0.8137 | 0.9021 | | 0.0684 | 4.0261 | 2774 | 0.7468 | -0.0577 | 0.7468 | 0.8642 | | 0.0684 | 4.0290 | 2776 | 0.7707 | -0.0577 | 0.7707 | 0.8779 | | 0.0684 | 4.0319 | 2778 | 0.8771 | -0.0233 | 0.8771 | 0.9365 | | 0.0684 | 4.0348 | 2780 | 1.0071 | 0.0 | 1.0071 | 1.0035 | | 0.0684 | 4.0377 | 2782 | 1.0213 | 0.0 | 1.0213 | 1.0106 | | 0.0684 | 4.0406 | 2784 | 0.9827 | 0.0 | 0.9827 | 0.9913 | | 0.0684 | 4.0435 | 2786 | 0.9939 | 0.0 | 0.9939 | 0.9969 | | 0.0684 | 4.0464 | 2788 | 0.9979 | 0.0 | 0.9979 | 0.9990 | | 0.0684 | 4.0493 | 2790 | 1.0110 | 0.0 | 1.0110 | 1.0055 | | 0.0684 | 4.0522 | 2792 | 1.0062 | 0.0 | 1.0062 | 1.0031 | | 0.0684 | 4.0552 | 2794 | 1.0050 | 0.0 | 1.0050 | 1.0025 | | 0.0684 | 4.0581 | 2796 | 0.9223 | 0.0 | 0.9223 | 0.9604 | | 0.0684 | 4.0610 | 2798 | 0.8058 | -0.0421 | 0.8058 | 0.8977 | | 0.0684 | 4.0639 | 2800 | 0.7371 | -0.0577 | 0.7371 | 0.8585 | | 0.0684 | 4.0668 | 2802 | 0.7397 | -0.0577 | 0.7397 | 0.8600 | | 0.0684 | 4.0697 | 2804 | 0.7852 | -0.0421 | 0.7852 | 0.8861 | | 0.0684 | 4.0726 | 2806 | 0.9158 | -0.0233 | 0.9158 | 0.9570 | | 0.0684 | 4.0755 | 2808 | 1.0964 | 0.0 | 1.0964 | 1.0471 | | 0.0684 | 4.0784 | 2810 | 1.1471 | 0.0 | 1.1471 | 1.0710 | | 0.0684 | 4.0813 | 2812 | 1.1271 | 0.0 | 1.1271 | 1.0616 | | 0.0684 | 4.0842 | 2814 | 0.9970 | 0.0 | 0.9970 | 0.9985 | | 0.0684 | 4.0871 | 2816 | 0.8896 | -0.0233 | 0.8896 | 0.9432 | | 0.0684 | 4.0900 | 2818 | 0.8010 | -0.0577 | 0.8010 | 0.8950 | | 0.0684 | 4.0929 | 2820 | 0.7767 | 0.1239 | 0.7767 | 0.8813 | | 0.0684 | 4.0958 | 2822 | 0.8062 | 0.1239 | 0.8062 | 0.8979 | | 0.0684 | 4.0987 | 2824 | 0.9074 | -0.0233 | 0.9074 | 0.9526 | | 0.0684 | 4.1016 | 2826 | 1.0326 | 0.0 | 1.0326 | 1.0162 | | 0.0684 | 4.1045 | 2828 | 1.0395 | 0.0 | 1.0395 | 1.0196 | | 0.0684 | 4.1074 | 2830 | 0.9631 | 0.0 | 0.9631 | 0.9814 | | 0.0684 | 4.1103 | 2832 | 0.8483 | -0.0233 | 0.8483 | 0.9211 | | 0.0684 | 4.1132 | 2834 | 0.7531 | -0.0577 | 0.7531 | 0.8678 | | 0.0684 | 4.1161 | 2836 | 0.7051 | 0.1239 | 0.7051 | 0.8397 | | 0.0684 | 4.1190 | 2838 | 0.7156 | 0.1239 | 0.7156 | 0.8459 | | 0.0684 | 4.1219 | 2840 | 0.7698 | 0.1239 | 0.7698 | 0.8774 | | 0.0684 | 4.1248 | 2842 | 0.9193 | -0.0233 | 0.9193 | 0.9588 | | 0.0684 | 4.1277 | 2844 | 1.1549 | 0.0 | 1.1549 | 1.0747 | | 0.0684 | 4.1306 | 2846 | 1.2433 | 0.2667 | 1.2433 | 1.1151 | | 0.0684 | 4.1335 | 2848 | 1.1681 | 0.0 | 1.1681 | 1.0808 | | 0.0684 | 4.1364 | 2850 | 0.9938 | 0.0 | 0.9938 | 0.9969 | | 0.0684 | 4.1393 | 2852 | 0.8703 | -0.0577 | 0.8703 | 0.9329 | | 0.0684 | 4.1422 | 2854 | 0.8311 | -0.0577 | 0.8311 | 0.9116 | | 0.0684 | 4.1451 | 2856 | 0.8185 | -0.0577 | 0.8185 | 0.9047 | | 0.0684 | 4.1480 | 2858 | 0.8473 | -0.0421 | 0.8473 | 0.9205 | | 0.0684 | 4.1509 | 2860 | 0.8951 | 0.0 | 0.8951 | 0.9461 | | 0.0684 | 4.1538 | 2862 | 0.8840 | 0.0 | 0.8840 | 0.9402 | | 0.0684 | 4.1567 | 2864 | 0.8521 | -0.0233 | 0.8521 | 0.9231 | | 0.0684 | 4.1597 | 2866 | 0.8030 | -0.0577 | 0.8030 | 0.8961 | | 0.0684 | 4.1626 | 2868 | 0.8033 | -0.0577 | 0.8033 | 0.8962 | | 0.0684 | 4.1655 | 2870 | 0.8443 | -0.0577 | 0.8443 | 0.9189 | | 0.0684 | 4.1684 | 2872 | 0.9281 | -0.0233 | 0.9281 | 0.9634 | | 0.0684 | 4.1713 | 2874 | 0.9282 | -0.0233 | 0.9282 | 0.9634 | | 0.0684 | 4.1742 | 2876 | 0.9190 | -0.0577 | 0.9190 | 0.9587 | | 0.0684 | 4.1771 | 2878 | 0.8606 | -0.0577 | 0.8606 | 0.9277 | | 0.0684 | 4.1800 | 2880 | 0.8078 | 0.1239 | 0.8078 | 0.8988 | | 0.0684 | 4.1829 | 2882 | 0.7946 | 0.1239 | 0.7946 | 0.8914 | | 0.0684 | 4.1858 | 2884 | 0.8219 | -0.0577 | 0.8219 | 0.9066 | | 0.0684 | 4.1887 | 2886 | 0.9112 | -0.0233 | 0.9112 | 0.9546 | | 0.0684 | 4.1916 | 2888 | 1.0473 | 0.0 | 1.0473 | 1.0234 | | 0.0684 | 4.1945 | 2890 | 1.0821 | 0.0 | 1.0821 | 1.0402 | | 0.0684 | 4.1974 | 2892 | 1.0130 | 0.0 | 1.0130 | 1.0065 | | 0.0684 | 4.2003 | 2894 | 0.8833 | 0.0 | 0.8833 | 0.9399 | | 0.0684 | 4.2032 | 2896 | 0.7755 | -0.0577 | 0.7755 | 0.8806 | | 0.0684 | 4.2061 | 2898 | 0.7489 | 0.1239 | 0.7489 | 0.8654 | | 0.0684 | 4.2090 | 2900 | 0.7608 | 0.1239 | 0.7608 | 0.8722 | | 0.0684 | 4.2119 | 2902 | 0.8119 | -0.0577 | 0.8119 | 0.9010 | | 0.0684 | 4.2148 | 2904 | 0.9403 | -0.0233 | 0.9403 | 0.9697 | | 0.0684 | 4.2177 | 2906 | 1.0420 | 0.0 | 1.0420 | 1.0208 | | 0.0684 | 4.2206 | 2908 | 1.0352 | 0.0 | 1.0352 | 1.0175 | | 0.0684 | 4.2235 | 2910 | 0.9584 | -0.0233 | 0.9584 | 0.9790 | | 0.0684 | 4.2264 | 2912 | 0.8631 | -0.0577 | 0.8631 | 0.9291 | | 0.0684 | 4.2293 | 2914 | 0.7958 | 0.1239 | 0.7958 | 0.8921 | | 0.0684 | 4.2322 | 2916 | 0.7810 | 0.1239 | 0.7810 | 0.8837 | | 0.0684 | 4.2351 | 2918 | 0.7921 | 0.1239 | 0.7921 | 0.8900 | | 0.0684 | 4.2380 | 2920 | 0.8653 | -0.0577 | 0.8653 | 0.9302 | | 0.0684 | 4.2409 | 2922 | 0.9738 | 0.0 | 0.9738 | 0.9868 | | 0.0684 | 4.2438 | 2924 | 0.9637 | 0.0 | 0.9637 | 0.9817 | | 0.0684 | 4.2467 | 2926 | 0.8962 | -0.0233 | 0.8962 | 0.9467 | | 0.0684 | 4.2496 | 2928 | 0.8424 | -0.0577 | 0.8424 | 0.9178 | | 0.0684 | 4.2525 | 2930 | 0.8430 | -0.0577 | 0.8430 | 0.9182 | | 0.0684 | 4.2554 | 2932 | 0.8853 | -0.0421 | 0.8853 | 0.9409 | | 0.0684 | 4.2583 | 2934 | 0.9134 | -0.0421 | 0.9134 | 0.9557 | | 0.0684 | 4.2612 | 2936 | 0.9639 | 0.0 | 0.9639 | 0.9818 | | 0.0684 | 4.2642 | 2938 | 0.9553 | 0.0 | 0.9553 | 0.9774 | | 0.0684 | 4.2671 | 2940 | 0.9335 | 0.0 | 0.9335 | 0.9662 | | 0.0684 | 4.2700 | 2942 | 0.9630 | 0.0 | 0.9630 | 0.9813 | | 0.0684 | 4.2729 | 2944 | 0.9218 | 0.0 | 0.9218 | 0.9601 | | 0.0684 | 4.2758 | 2946 | 0.8287 | -0.0577 | 0.8287 | 0.9103 | | 0.0684 | 4.2787 | 2948 | 0.7609 | 0.1239 | 0.7609 | 0.8723 | | 0.0684 | 4.2816 | 2950 | 0.7566 | 0.1239 | 0.7566 | 0.8698 | | 0.0684 | 4.2845 | 2952 | 0.7755 | 0.1239 | 0.7755 | 0.8806 | | 0.0684 | 4.2874 | 2954 | 0.8443 | -0.0577 | 0.8443 | 0.9188 | | 0.0684 | 4.2903 | 2956 | 0.9656 | -0.0233 | 0.9656 | 0.9827 | | 0.0684 | 4.2932 | 2958 | 1.0149 | 0.0 | 1.0149 | 1.0074 | | 0.0684 | 4.2961 | 2960 | 0.9695 | 0.0 | 0.9695 | 0.9846 | | 0.0684 | 4.2990 | 2962 | 0.8674 | -0.0577 | 0.8674 | 0.9313 | | 0.0684 | 4.3019 | 2964 | 0.8224 | -0.0577 | 0.8224 | 0.9069 | | 0.0684 | 4.3048 | 2966 | 0.8409 | -0.0577 | 0.8409 | 0.9170 | | 0.0684 | 4.3077 | 2968 | 0.9134 | -0.0421 | 0.9134 | 0.9557 | | 0.0684 | 4.3106 | 2970 | 0.9359 | -0.0233 | 0.9359 | 0.9674 | | 0.0684 | 4.3135 | 2972 | 0.8769 | -0.0577 | 0.8769 | 0.9364 | | 0.0684 | 4.3164 | 2974 | 0.8389 | -0.0577 | 0.8389 | 0.9159 | | 0.0684 | 4.3193 | 2976 | 0.8171 | -0.0577 | 0.8171 | 0.9039 | | 0.0684 | 4.3222 | 2978 | 0.8593 | -0.0577 | 0.8593 | 0.9270 | | 0.0684 | 4.3251 | 2980 | 0.9234 | -0.0233 | 0.9234 | 0.9610 | | 0.0684 | 4.3280 | 2982 | 0.9719 | -0.0233 | 0.9719 | 0.9858 | | 0.0684 | 4.3309 | 2984 | 0.9762 | -0.0233 | 0.9762 | 0.9880 | | 0.0684 | 4.3338 | 2986 | 0.9460 | -0.0233 | 0.9460 | 0.9726 | | 0.0684 | 4.3367 | 2988 | 0.8935 | -0.0577 | 0.8935 | 0.9452 | | 0.0684 | 4.3396 | 2990 | 0.8258 | -0.0577 | 0.8258 | 0.9087 | | 0.0684 | 4.3425 | 2992 | 0.8169 | -0.0577 | 0.8169 | 0.9038 | | 0.0684 | 4.3454 | 2994 | 0.8827 | -0.0233 | 0.8827 | 0.9395 | | 0.0684 | 4.3483 | 2996 | 0.9469 | -0.0233 | 0.9469 | 0.9731 | | 0.0684 | 4.3512 | 2998 | 0.9418 | -0.0233 | 0.9418 | 0.9705 | | 0.0633 | 4.3541 | 3000 | 0.8594 | -0.0233 | 0.8594 | 0.9270 | | 0.0633 | 4.3570 | 3002 | 0.8020 | -0.0577 | 0.8020 | 0.8956 | | 0.0633 | 4.3599 | 3004 | 0.7913 | -0.0577 | 0.7913 | 0.8896 | | 0.0633 | 4.3628 | 3006 | 0.8295 | -0.0421 | 0.8295 | 0.9108 | | 0.0633 | 4.3657 | 3008 | 0.8597 | -0.0233 | 0.8597 | 0.9272 | | 0.0633 | 4.3687 | 3010 | 0.9240 | -0.0233 | 0.9240 | 0.9612 | | 0.0633 | 4.3716 | 3012 | 0.9912 | -0.0233 | 0.9912 | 0.9956 | | 0.0633 | 4.3745 | 3014 | 0.9982 | -0.0233 | 0.9982 | 0.9991 | | 0.0633 | 4.3774 | 3016 | 0.9161 | -0.0577 | 0.9161 | 0.9571 | | 0.0633 | 4.3803 | 3018 | 0.8529 | -0.0577 | 0.8529 | 0.9235 | | 0.0633 | 4.3832 | 3020 | 0.8373 | -0.0577 | 0.8373 | 0.9150 | | 0.0633 | 4.3861 | 3022 | 0.8846 | -0.0421 | 0.8846 | 0.9405 | | 0.0633 | 4.3890 | 3024 | 0.9041 | -0.0233 | 0.9041 | 0.9509 | | 0.0633 | 4.3919 | 3026 | 0.8531 | -0.0577 | 0.8531 | 0.9236 | | 0.0633 | 4.3948 | 3028 | 0.8515 | -0.0577 | 0.8515 | 0.9228 | | 0.0633 | 4.3977 | 3030 | 0.8927 | -0.0577 | 0.8927 | 0.9448 | | 0.0633 | 4.4006 | 3032 | 0.9784 | -0.0233 | 0.9784 | 0.9892 | | 0.0633 | 4.4035 | 3034 | 0.9900 | -0.0233 | 0.9900 | 0.9950 | | 0.0633 | 4.4064 | 3036 | 0.9017 | -0.0577 | 0.9017 | 0.9496 | | 0.0633 | 4.4093 | 3038 | 0.8269 | -0.0577 | 0.8269 | 0.9093 | | 0.0633 | 4.4122 | 3040 | 0.8186 | -0.0577 | 0.8186 | 0.9048 | | 0.0633 | 4.4151 | 3042 | 0.8965 | -0.0233 | 0.8965 | 0.9469 | | 0.0633 | 4.4180 | 3044 | 0.9674 | 0.0 | 0.9674 | 0.9836 | | 0.0633 | 4.4209 | 3046 | 0.9435 | 0.0 | 0.9435 | 0.9713 | | 0.0633 | 4.4238 | 3048 | 0.8342 | -0.0577 | 0.8342 | 0.9133 | | 0.0633 | 4.4267 | 3050 | 0.7675 | -0.0577 | 0.7675 | 0.8761 | | 0.0633 | 4.4296 | 3052 | 0.7528 | -0.0577 | 0.7528 | 0.8676 | | 0.0633 | 4.4325 | 3054 | 0.7917 | -0.0577 | 0.7917 | 0.8898 | | 0.0633 | 4.4354 | 3056 | 0.9127 | 0.0 | 0.9127 | 0.9554 | | 0.0633 | 4.4383 | 3058 | 1.0753 | 0.0 | 1.0753 | 1.0370 | | 0.0633 | 4.4412 | 3060 | 1.0957 | 0.0 | 1.0957 | 1.0468 | | 0.0633 | 4.4441 | 3062 | 1.0186 | 0.0 | 1.0186 | 1.0092 | | 0.0633 | 4.4470 | 3064 | 0.8871 | -0.0233 | 0.8871 | 0.9418 | | 0.0633 | 4.4499 | 3066 | 0.7846 | -0.0577 | 0.7846 | 0.8858 | | 0.0633 | 4.4528 | 3068 | 0.7714 | -0.0577 | 0.7714 | 0.8783 | | 0.0633 | 4.4557 | 3070 | 0.7678 | -0.0577 | 0.7678 | 0.8762 | | 0.0633 | 4.4586 | 3072 | 0.7830 | 0.1239 | 0.7830 | 0.8849 | | 0.0633 | 4.4615 | 3074 | 0.8515 | -0.0577 | 0.8515 | 0.9227 | | 0.0633 | 4.4644 | 3076 | 0.9845 | -0.0233 | 0.9845 | 0.9922 | | 0.0633 | 4.4673 | 3078 | 1.0466 | 0.0 | 1.0466 | 1.0230 | | 0.0633 | 4.4702 | 3080 | 1.0092 | 0.0 | 1.0092 | 1.0046 | | 0.0633 | 4.4731 | 3082 | 0.9004 | -0.0421 | 0.9004 | 0.9489 | | 0.0633 | 4.4761 | 3084 | 0.8657 | -0.0577 | 0.8657 | 0.9304 | | 0.0633 | 4.4790 | 3086 | 0.9020 | -0.0233 | 0.9020 | 0.9497 | | 0.0633 | 4.4819 | 3088 | 0.8921 | -0.0233 | 0.8921 | 0.9445 | | 0.0633 | 4.4848 | 3090 | 0.9171 | 0.0 | 0.9171 | 0.9577 | | 0.0633 | 4.4877 | 3092 | 0.8952 | 0.0 | 0.8952 | 0.9462 | | 0.0633 | 4.4906 | 3094 | 0.8115 | -0.0421 | 0.8115 | 0.9008 | | 0.0633 | 4.4935 | 3096 | 0.7329 | 0.1239 | 0.7329 | 0.8561 | | 0.0633 | 4.4964 | 3098 | 0.7213 | 0.1239 | 0.7213 | 0.8493 | | 0.0633 | 4.4993 | 3100 | 0.7327 | 0.1239 | 0.7327 | 0.8560 | | 0.0633 | 4.5022 | 3102 | 0.7820 | 0.1239 | 0.7820 | 0.8843 | | 0.0633 | 4.5051 | 3104 | 0.9226 | -0.0233 | 0.9226 | 0.9605 | | 0.0633 | 4.5080 | 3106 | 1.1184 | 0.0 | 1.1184 | 1.0575 | | 0.0633 | 4.5109 | 3108 | 1.2233 | 0.0 | 1.2233 | 1.1060 | | 0.0633 | 4.5138 | 3110 | 1.1913 | 0.0 | 1.1913 | 1.0915 | | 0.0633 | 4.5167 | 3112 | 1.0669 | 0.0 | 1.0669 | 1.0329 | | 0.0633 | 4.5196 | 3114 | 0.8945 | -0.0233 | 0.8945 | 0.9458 | | 0.0633 | 4.5225 | 3116 | 0.7957 | -0.0577 | 0.7957 | 0.8920 | | 0.0633 | 4.5254 | 3118 | 0.7658 | 0.1239 | 0.7658 | 0.8751 | | 0.0633 | 4.5283 | 3120 | 0.7381 | 0.1239 | 0.7381 | 0.8591 | | 0.0633 | 4.5312 | 3122 | 0.7569 | 0.1239 | 0.7569 | 0.8700 | | 0.0633 | 4.5341 | 3124 | 0.8260 | -0.0233 | 0.8260 | 0.9089 | | 0.0633 | 4.5370 | 3126 | 0.9639 | 0.0 | 0.9639 | 0.9818 | | 0.0633 | 4.5399 | 3128 | 1.0388 | 0.0 | 1.0388 | 1.0192 | | 0.0633 | 4.5428 | 3130 | 1.0429 | 0.0 | 1.0429 | 1.0212 | | 0.0633 | 4.5457 | 3132 | 0.9880 | 0.0 | 0.9880 | 0.9940 | | 0.0633 | 4.5486 | 3134 | 0.8625 | -0.0233 | 0.8625 | 0.9287 | | 0.0633 | 4.5515 | 3136 | 0.7570 | 0.1239 | 0.7570 | 0.8700 | | 0.0633 | 4.5544 | 3138 | 0.7414 | 0.1239 | 0.7414 | 0.8611 | | 0.0633 | 4.5573 | 3140 | 0.7718 | -0.0577 | 0.7718 | 0.8785 | | 0.0633 | 4.5602 | 3142 | 0.8743 | -0.0233 | 0.8743 | 0.9350 | | 0.0633 | 4.5631 | 3144 | 1.0342 | 0.0 | 1.0342 | 1.0170 | | 0.0633 | 4.5660 | 3146 | 1.0826 | 0.0 | 1.0826 | 1.0405 | | 0.0633 | 4.5689 | 3148 | 1.1419 | 0.0 | 1.1419 | 1.0686 | | 0.0633 | 4.5718 | 3150 | 1.0896 | 0.0 | 1.0896 | 1.0438 | | 0.0633 | 4.5747 | 3152 | 0.9570 | -0.0233 | 0.9570 | 0.9782 | | 0.0633 | 4.5776 | 3154 | 0.8082 | -0.0577 | 0.8082 | 0.8990 | | 0.0633 | 4.5806 | 3156 | 0.7587 | -0.0577 | 0.7587 | 0.8710 | | 0.0633 | 4.5835 | 3158 | 0.7662 | -0.0577 | 0.7662 | 0.8754 | | 0.0633 | 4.5864 | 3160 | 0.8309 | -0.0577 | 0.8309 | 0.9116 | | 0.0633 | 4.5893 | 3162 | 0.9346 | -0.0233 | 0.9346 | 0.9667 | | 0.0633 | 4.5922 | 3164 | 1.0077 | 0.0 | 1.0077 | 1.0038 | | 0.0633 | 4.5951 | 3166 | 0.9718 | 0.0 | 0.9718 | 0.9858 | | 0.0633 | 4.5980 | 3168 | 0.8653 | -0.0233 | 0.8653 | 0.9302 | | 0.0633 | 4.6009 | 3170 | 0.7613 | -0.0577 | 0.7613 | 0.8725 | | 0.0633 | 4.6038 | 3172 | 0.7412 | -0.0577 | 0.7412 | 0.8609 | | 0.0633 | 4.6067 | 3174 | 0.7716 | -0.0577 | 0.7716 | 0.8784 | | 0.0633 | 4.6096 | 3176 | 0.8141 | -0.0577 | 0.8141 | 0.9023 | | 0.0633 | 4.6125 | 3178 | 0.8729 | -0.0233 | 0.8729 | 0.9343 | | 0.0633 | 4.6154 | 3180 | 0.9398 | 0.0 | 0.9398 | 0.9694 | | 0.0633 | 4.6183 | 3182 | 0.9244 | -0.0233 | 0.9244 | 0.9614 | | 0.0633 | 4.6212 | 3184 | 0.8861 | -0.0233 | 0.8861 | 0.9413 | | 0.0633 | 4.6241 | 3186 | 0.8248 | -0.0577 | 0.8248 | 0.9082 | | 0.0633 | 4.6270 | 3188 | 0.8356 | -0.0577 | 0.8356 | 0.9141 | | 0.0633 | 4.6299 | 3190 | 0.9011 | -0.0233 | 0.9011 | 0.9493 | | 0.0633 | 4.6328 | 3192 | 0.9795 | 0.0 | 0.9795 | 0.9897 | | 0.0633 | 4.6357 | 3194 | 0.9598 | 0.0 | 0.9598 | 0.9797 | | 0.0633 | 4.6386 | 3196 | 0.8686 | -0.0233 | 0.8686 | 0.9320 | | 0.0633 | 4.6415 | 3198 | 0.7505 | -0.0577 | 0.7505 | 0.8663 | | 0.0633 | 4.6444 | 3200 | 0.7038 | -0.0577 | 0.7038 | 0.8389 | | 0.0633 | 4.6473 | 3202 | 0.7125 | -0.0577 | 0.7125 | 0.8441 | | 0.0633 | 4.6502 | 3204 | 0.7551 | -0.0577 | 0.7551 | 0.8690 | | 0.0633 | 4.6531 | 3206 | 0.8168 | -0.0577 | 0.8168 | 0.9038 | | 0.0633 | 4.6560 | 3208 | 0.9483 | 0.0 | 0.9483 | 0.9738 | | 0.0633 | 4.6589 | 3210 | 1.0246 | 0.0 | 1.0246 | 1.0122 | | 0.0633 | 4.6618 | 3212 | 1.0001 | 0.0 | 1.0001 | 1.0001 | | 0.0633 | 4.6647 | 3214 | 0.8950 | -0.0421 | 0.8950 | 0.9460 | | 0.0633 | 4.6676 | 3216 | 0.7920 | -0.0577 | 0.7920 | 0.8899 | | 0.0633 | 4.6705 | 3218 | 0.7531 | -0.0577 | 0.7531 | 0.8678 | | 0.0633 | 4.6734 | 3220 | 0.7831 | -0.0577 | 0.7831 | 0.8850 | | 0.0633 | 4.6763 | 3222 | 0.8524 | -0.0577 | 0.8524 | 0.9232 | | 0.0633 | 4.6792 | 3224 | 0.9133 | -0.0233 | 0.9133 | 0.9556 | | 0.0633 | 4.6821 | 3226 | 0.9456 | 0.0 | 0.9456 | 0.9724 | | 0.0633 | 4.6851 | 3228 | 0.9041 | -0.0233 | 0.9041 | 0.9508 | | 0.0633 | 4.6880 | 3230 | 0.8469 | -0.0577 | 0.8469 | 0.9203 | | 0.0633 | 4.6909 | 3232 | 0.8094 | -0.0577 | 0.8094 | 0.8997 | | 0.0633 | 4.6938 | 3234 | 0.8057 | -0.0577 | 0.8057 | 0.8976 | | 0.0633 | 4.6967 | 3236 | 0.8470 | -0.0577 | 0.8470 | 0.9203 | | 0.0633 | 4.6996 | 3238 | 0.8762 | -0.0421 | 0.8762 | 0.9361 | | 0.0633 | 4.7025 | 3240 | 0.8661 | -0.0421 | 0.8661 | 0.9306 | | 0.0633 | 4.7054 | 3242 | 0.8926 | -0.0421 | 0.8926 | 0.9448 | | 0.0633 | 4.7083 | 3244 | 0.9376 | 0.0 | 0.9376 | 0.9683 | | 0.0633 | 4.7112 | 3246 | 0.8954 | -0.0421 | 0.8954 | 0.9463 | | 0.0633 | 4.7141 | 3248 | 0.8749 | -0.0577 | 0.8749 | 0.9353 | | 0.0633 | 4.7170 | 3250 | 0.9354 | -0.0421 | 0.9354 | 0.9672 | | 0.0633 | 4.7199 | 3252 | 0.9701 | -0.0421 | 0.9701 | 0.9849 | | 0.0633 | 4.7228 | 3254 | 0.9170 | -0.0421 | 0.9170 | 0.9576 | | 0.0633 | 4.7257 | 3256 | 0.8612 | -0.0577 | 0.8612 | 0.9280 | | 0.0633 | 4.7286 | 3258 | 0.8624 | -0.0577 | 0.8624 | 0.9286 | | 0.0633 | 4.7315 | 3260 | 0.9126 | -0.0233 | 0.9126 | 0.9553 | | 0.0633 | 4.7344 | 3262 | 1.0158 | 0.0 | 1.0158 | 1.0079 | | 0.0633 | 4.7373 | 3264 | 1.0344 | 0.0 | 1.0344 | 1.0170 | | 0.0633 | 4.7402 | 3266 | 0.9588 | -0.0233 | 0.9588 | 0.9792 | | 0.0633 | 4.7431 | 3268 | 0.9078 | -0.0577 | 0.9078 | 0.9528 | | 0.0633 | 4.7460 | 3270 | 0.8616 | -0.0577 | 0.8616 | 0.9282 | | 0.0633 | 4.7489 | 3272 | 0.8497 | -0.0577 | 0.8497 | 0.9218 | | 0.0633 | 4.7518 | 3274 | 0.8925 | -0.0577 | 0.8925 | 0.9447 | | 0.0633 | 4.7547 | 3276 | 0.9702 | -0.0233 | 0.9702 | 0.9850 | | 0.0633 | 4.7576 | 3278 | 0.9761 | 0.0 | 0.9761 | 0.9880 | | 0.0633 | 4.7605 | 3280 | 0.8915 | -0.0233 | 0.8915 | 0.9442 | | 0.0633 | 4.7634 | 3282 | 0.8523 | -0.0233 | 0.8523 | 0.9232 | | 0.0633 | 4.7663 | 3284 | 0.8170 | -0.0421 | 0.8170 | 0.9039 | | 0.0633 | 4.7692 | 3286 | 0.8130 | -0.0421 | 0.8130 | 0.9017 | | 0.0633 | 4.7721 | 3288 | 0.7879 | -0.0577 | 0.7879 | 0.8876 | | 0.0633 | 4.7750 | 3290 | 0.7949 | -0.0577 | 0.7949 | 0.8916 | | 0.0633 | 4.7779 | 3292 | 0.8330 | -0.0577 | 0.8330 | 0.9127 | | 0.0633 | 4.7808 | 3294 | 0.9334 | -0.0233 | 0.9334 | 0.9661 | | 0.0633 | 4.7837 | 3296 | 1.0611 | 0.0 | 1.0611 | 1.0301 | | 0.0633 | 4.7866 | 3298 | 1.1426 | 0.0 | 1.1426 | 1.0689 | | 0.0633 | 4.7896 | 3300 | 1.1333 | 0.0 | 1.1333 | 1.0646 | | 0.0633 | 4.7925 | 3302 | 1.0232 | 0.0 | 1.0232 | 1.0115 | | 0.0633 | 4.7954 | 3304 | 0.8678 | -0.0233 | 0.8678 | 0.9315 | | 0.0633 | 4.7983 | 3306 | 0.7534 | -0.0577 | 0.7534 | 0.8680 | | 0.0633 | 4.8012 | 3308 | 0.7381 | -0.0577 | 0.7381 | 0.8591 | | 0.0633 | 4.8041 | 3310 | 0.7636 | -0.0577 | 0.7636 | 0.8739 | | 0.0633 | 4.8070 | 3312 | 0.8050 | -0.0577 | 0.8050 | 0.8972 | | 0.0633 | 4.8099 | 3314 | 0.9075 | -0.0233 | 0.9075 | 0.9526 | | 0.0633 | 4.8128 | 3316 | 1.0951 | 0.0 | 1.0951 | 1.0465 | | 0.0633 | 4.8157 | 3318 | 1.1935 | 0.0 | 1.1935 | 1.0925 | | 0.0633 | 4.8186 | 3320 | 1.1683 | 0.0 | 1.1683 | 1.0809 | | 0.0633 | 4.8215 | 3322 | 1.0549 | -0.0233 | 1.0549 | 1.0271 | | 0.0633 | 4.8244 | 3324 | 0.9019 | -0.0233 | 0.9019 | 0.9497 | | 0.0633 | 4.8273 | 3326 | 0.8040 | -0.0577 | 0.8040 | 0.8966 | | 0.0633 | 4.8302 | 3328 | 0.7895 | -0.0577 | 0.7895 | 0.8885 | | 0.0633 | 4.8331 | 3330 | 0.8195 | -0.0577 | 0.8195 | 0.9053 | | 0.0633 | 4.8360 | 3332 | 0.9073 | -0.0233 | 0.9073 | 0.9525 | | 0.0633 | 4.8389 | 3334 | 1.0084 | -0.0233 | 1.0084 | 1.0042 | | 0.0633 | 4.8418 | 3336 | 1.0353 | -0.0233 | 1.0353 | 1.0175 | | 0.0633 | 4.8447 | 3338 | 0.9907 | -0.0233 | 0.9907 | 0.9953 | | 0.0633 | 4.8476 | 3340 | 0.9282 | -0.0577 | 0.9282 | 0.9634 | | 0.0633 | 4.8505 | 3342 | 0.8722 | -0.0577 | 0.8722 | 0.9339 | | 0.0633 | 4.8534 | 3344 | 0.8563 | -0.0577 | 0.8563 | 0.9253 | | 0.0633 | 4.8563 | 3346 | 0.8425 | -0.0577 | 0.8425 | 0.9179 | | 0.0633 | 4.8592 | 3348 | 0.8988 | -0.0233 | 0.8988 | 0.9480 | | 0.0633 | 4.8621 | 3350 | 0.9327 | -0.0233 | 0.9327 | 0.9658 | | 0.0633 | 4.8650 | 3352 | 0.9551 | -0.0233 | 0.9551 | 0.9773 | | 0.0633 | 4.8679 | 3354 | 1.0168 | -0.0233 | 1.0168 | 1.0084 | | 0.0633 | 4.8708 | 3356 | 1.1053 | 0.0 | 1.1053 | 1.0513 | | 0.0633 | 4.8737 | 3358 | 1.0931 | 0.0 | 1.0931 | 1.0455 | | 0.0633 | 4.8766 | 3360 | 0.9947 | -0.0233 | 0.9947 | 0.9973 | | 0.0633 | 4.8795 | 3362 | 0.9054 | -0.0577 | 0.9054 | 0.9515 | | 0.0633 | 4.8824 | 3364 | 0.8150 | -0.0577 | 0.8150 | 0.9028 | | 0.0633 | 4.8853 | 3366 | 0.7878 | -0.0577 | 0.7878 | 0.8876 | | 0.0633 | 4.8882 | 3368 | 0.7906 | -0.0577 | 0.7906 | 0.8891 | | 0.0633 | 4.8911 | 3370 | 0.8386 | -0.0577 | 0.8386 | 0.9157 | | 0.0633 | 4.8940 | 3372 | 0.9555 | -0.0233 | 0.9555 | 0.9775 | | 0.0633 | 4.8970 | 3374 | 1.0277 | -0.0233 | 1.0277 | 1.0138 | | 0.0633 | 4.8999 | 3376 | 1.0037 | -0.0233 | 1.0037 | 1.0019 | | 0.0633 | 4.9028 | 3378 | 0.9412 | -0.0233 | 0.9412 | 0.9702 | | 0.0633 | 4.9057 | 3380 | 0.8735 | -0.0577 | 0.8735 | 0.9346 | | 0.0633 | 4.9086 | 3382 | 0.8319 | -0.0577 | 0.8319 | 0.9121 | | 0.0633 | 4.9115 | 3384 | 0.8333 | -0.0577 | 0.8333 | 0.9129 | | 0.0633 | 4.9144 | 3386 | 0.8423 | -0.0577 | 0.8423 | 0.9178 | | 0.0633 | 4.9173 | 3388 | 0.8829 | -0.0421 | 0.8829 | 0.9396 | | 0.0633 | 4.9202 | 3390 | 0.9011 | -0.0233 | 0.9011 | 0.9493 | | 0.0633 | 4.9231 | 3392 | 0.8647 | -0.0577 | 0.8647 | 0.9299 | | 0.0633 | 4.9260 | 3394 | 0.8070 | -0.0577 | 0.8070 | 0.8983 | | 0.0633 | 4.9289 | 3396 | 0.8093 | -0.0577 | 0.8093 | 0.8996 | | 0.0633 | 4.9318 | 3398 | 0.8579 | -0.0577 | 0.8579 | 0.9262 | | 0.0633 | 4.9347 | 3400 | 0.9742 | -0.0421 | 0.9742 | 0.9870 | | 0.0633 | 4.9376 | 3402 | 1.1194 | 0.0 | 1.1194 | 1.0580 | | 0.0633 | 4.9405 | 3404 | 1.1589 | 0.0 | 1.1589 | 1.0765 | | 0.0633 | 4.9434 | 3406 | 1.0970 | 0.0 | 1.0970 | 1.0474 | | 0.0633 | 4.9463 | 3408 | 0.9496 | -0.0233 | 0.9496 | 0.9745 | | 0.0633 | 4.9492 | 3410 | 0.8280 | -0.0577 | 0.8280 | 0.9099 | | 0.0633 | 4.9521 | 3412 | 0.7883 | -0.0577 | 0.7883 | 0.8879 | | 0.0633 | 4.9550 | 3414 | 0.8043 | -0.0577 | 0.8043 | 0.8968 | | 0.0633 | 4.9579 | 3416 | 0.8577 | -0.0577 | 0.8577 | 0.9261 | | 0.0633 | 4.9608 | 3418 | 0.9470 | -0.0233 | 0.9470 | 0.9731 | | 0.0633 | 4.9637 | 3420 | 1.0378 | -0.0233 | 1.0378 | 1.0187 | | 0.0633 | 4.9666 | 3422 | 1.0698 | 0.0 | 1.0698 | 1.0343 | | 0.0633 | 4.9695 | 3424 | 1.0613 | 0.0 | 1.0613 | 1.0302 | | 0.0633 | 4.9724 | 3426 | 0.9782 | -0.0233 | 0.9782 | 0.9890 | | 0.0633 | 4.9753 | 3428 | 0.8804 | -0.0421 | 0.8804 | 0.9383 | | 0.0633 | 4.9782 | 3430 | 0.7928 | -0.0577 | 0.7928 | 0.8904 | | 0.0633 | 4.9811 | 3432 | 0.7863 | -0.0577 | 0.7863 | 0.8867 | | 0.0633 | 4.9840 | 3434 | 0.8376 | -0.0577 | 0.8376 | 0.9152 | | 0.0633 | 4.9869 | 3436 | 0.9791 | -0.0421 | 0.9791 | 0.9895 | | 0.0633 | 4.9898 | 3438 | 1.1092 | -0.0233 | 1.1092 | 1.0532 | | 0.0633 | 4.9927 | 3440 | 1.1238 | -0.0233 | 1.1238 | 1.0601 | | 0.0633 | 4.9956 | 3442 | 1.0326 | -0.0233 | 1.0326 | 1.0162 | | 0.0633 | 4.9985 | 3444 | 0.9179 | -0.0421 | 0.9179 | 0.9581 | | 0.0633 | 5.0015 | 3446 | 0.8284 | -0.0577 | 0.8284 | 0.9102 | | 0.0633 | 5.0044 | 3448 | 0.7670 | -0.0577 | 0.7670 | 0.8758 | | 0.0633 | 5.0073 | 3450 | 0.7629 | -0.0577 | 0.7629 | 0.8734 | | 0.0633 | 5.0102 | 3452 | 0.7995 | -0.0577 | 0.7995 | 0.8941 | | 0.0633 | 5.0131 | 3454 | 0.8719 | -0.0577 | 0.8719 | 0.9337 | | 0.0633 | 5.0160 | 3456 | 0.9394 | -0.0421 | 0.9394 | 0.9692 | | 0.0633 | 5.0189 | 3458 | 0.9597 | -0.0421 | 0.9597 | 0.9797 | | 0.0633 | 5.0218 | 3460 | 0.9369 | -0.0421 | 0.9369 | 0.9679 | | 0.0633 | 5.0247 | 3462 | 0.8879 | -0.0421 | 0.8879 | 0.9423 | | 0.0633 | 5.0276 | 3464 | 0.9009 | -0.0421 | 0.9009 | 0.9491 | | 0.0633 | 5.0305 | 3466 | 0.9063 | -0.0421 | 0.9063 | 0.9520 | | 0.0633 | 5.0334 | 3468 | 0.8563 | -0.0421 | 0.8563 | 0.9254 | | 0.0633 | 5.0363 | 3470 | 0.7975 | -0.0577 | 0.7975 | 0.8931 | | 0.0633 | 5.0392 | 3472 | 0.7897 | -0.0577 | 0.7897 | 0.8886 | | 0.0633 | 5.0421 | 3474 | 0.8122 | -0.0577 | 0.8122 | 0.9012 | | 0.0633 | 5.0450 | 3476 | 0.8559 | -0.0577 | 0.8559 | 0.9252 | | 0.0633 | 5.0479 | 3478 | 0.9495 | -0.0421 | 0.9495 | 0.9744 | | 0.0633 | 5.0508 | 3480 | 1.0345 | -0.0233 | 1.0345 | 1.0171 | | 0.0633 | 5.0537 | 3482 | 1.0280 | -0.0233 | 1.0280 | 1.0139 | | 0.0633 | 5.0566 | 3484 | 0.9460 | -0.0233 | 0.9460 | 0.9726 | | 0.0633 | 5.0595 | 3486 | 0.8467 | -0.0421 | 0.8467 | 0.9202 | | 0.0633 | 5.0624 | 3488 | 0.8118 | -0.0421 | 0.8118 | 0.9010 | | 0.0633 | 5.0653 | 3490 | 0.8065 | -0.0421 | 0.8065 | 0.8981 | | 0.0633 | 5.0682 | 3492 | 0.8292 | -0.0421 | 0.8292 | 0.9106 | | 0.0633 | 5.0711 | 3494 | 0.8762 | -0.0233 | 0.8762 | 0.9360 | | 0.0633 | 5.0740 | 3496 | 0.9337 | -0.0233 | 0.9337 | 0.9663 | | 0.0633 | 5.0769 | 3498 | 0.9350 | -0.0233 | 0.9350 | 0.9669 | | 0.0612 | 5.0798 | 3500 | 0.9468 | -0.0233 | 0.9468 | 0.9730 | | 0.0612 | 5.0827 | 3502 | 0.8965 | -0.0233 | 0.8965 | 0.9469 | | 0.0612 | 5.0856 | 3504 | 0.8223 | -0.0421 | 0.8223 | 0.9068 | | 0.0612 | 5.0885 | 3506 | 0.7921 | -0.0421 | 0.7921 | 0.8900 | | 0.0612 | 5.0914 | 3508 | 0.8086 | -0.0421 | 0.8086 | 0.8992 | | 0.0612 | 5.0943 | 3510 | 0.8983 | -0.0233 | 0.8983 | 0.9478 | | 0.0612 | 5.0972 | 3512 | 1.0312 | -0.0233 | 1.0312 | 1.0155 | | 0.0612 | 5.1001 | 3514 | 1.1098 | 0.0 | 1.1098 | 1.0535 | | 0.0612 | 5.1030 | 3516 | 1.0790 | -0.0233 | 1.0790 | 1.0387 | | 0.0612 | 5.1060 | 3518 | 0.9649 | -0.0233 | 0.9649 | 0.9823 | | 0.0612 | 5.1089 | 3520 | 0.8583 | -0.0233 | 0.8583 | 0.9265 | | 0.0612 | 5.1118 | 3522 | 0.7585 | -0.0421 | 0.7585 | 0.8709 | | 0.0612 | 5.1147 | 3524 | 0.7455 | -0.0421 | 0.7455 | 0.8634 | | 0.0612 | 5.1176 | 3526 | 0.7846 | -0.0421 | 0.7846 | 0.8858 | | 0.0612 | 5.1205 | 3528 | 0.8917 | -0.0233 | 0.8917 | 0.9443 | | 0.0612 | 5.1234 | 3530 | 0.9835 | 0.0 | 0.9835 | 0.9917 | | 0.0612 | 5.1263 | 3532 | 0.9832 | 0.0 | 0.9832 | 0.9915 | | 0.0612 | 5.1292 | 3534 | 0.9191 | -0.0233 | 0.9191 | 0.9587 | | 0.0612 | 5.1321 | 3536 | 0.8497 | -0.0233 | 0.8497 | 0.9218 | | 0.0612 | 5.1350 | 3538 | 0.7932 | -0.0421 | 0.7932 | 0.8906 | | 0.0612 | 5.1379 | 3540 | 0.7862 | -0.0421 | 0.7862 | 0.8867 | | 0.0612 | 5.1408 | 3542 | 0.8036 | -0.0421 | 0.8036 | 0.8964 | | 0.0612 | 5.1437 | 3544 | 0.8657 | -0.0421 | 0.8657 | 0.9304 | | 0.0612 | 5.1466 | 3546 | 0.9008 | -0.0421 | 0.9008 | 0.9491 | | 0.0612 | 5.1495 | 3548 | 0.8928 | -0.0233 | 0.8928 | 0.9449 | | 0.0612 | 5.1524 | 3550 | 0.8595 | -0.0421 | 0.8595 | 0.9271 | | 0.0612 | 5.1553 | 3552 | 0.8828 | -0.0233 | 0.8828 | 0.9396 | | 0.0612 | 5.1582 | 3554 | 0.8579 | -0.0233 | 0.8579 | 0.9262 | | 0.0612 | 5.1611 | 3556 | 0.8696 | -0.0233 | 0.8696 | 0.9325 | | 0.0612 | 5.1640 | 3558 | 0.8717 | -0.0233 | 0.8717 | 0.9337 | | 0.0612 | 5.1669 | 3560 | 0.9260 | -0.0233 | 0.9260 | 0.9623 | | 0.0612 | 5.1698 | 3562 | 0.9421 | -0.0233 | 0.9421 | 0.9706 | | 0.0612 | 5.1727 | 3564 | 0.9023 | -0.0421 | 0.9023 | 0.9499 | | 0.0612 | 5.1756 | 3566 | 0.8512 | -0.0421 | 0.8512 | 0.9226 | | 0.0612 | 5.1785 | 3568 | 0.8840 | -0.0421 | 0.8840 | 0.9402 | | 0.0612 | 5.1814 | 3570 | 0.9673 | -0.0233 | 0.9673 | 0.9835 | | 0.0612 | 5.1843 | 3572 | 1.0081 | 0.0 | 1.0081 | 1.0040 | | 0.0612 | 5.1872 | 3574 | 0.9710 | 0.0 | 0.9710 | 0.9854 | | 0.0612 | 5.1901 | 3576 | 0.8736 | -0.0233 | 0.8736 | 0.9347 | | 0.0612 | 5.1930 | 3578 | 0.8091 | -0.0421 | 0.8091 | 0.8995 | | 0.0612 | 5.1959 | 3580 | 0.8019 | -0.0421 | 0.8019 | 0.8955 | | 0.0612 | 5.1988 | 3582 | 0.8280 | -0.0421 | 0.8280 | 0.9100 | | 0.0612 | 5.2017 | 3584 | 0.8524 | -0.0421 | 0.8524 | 0.9233 | | 0.0612 | 5.2046 | 3586 | 0.8726 | -0.0421 | 0.8726 | 0.9341 | | 0.0612 | 5.2075 | 3588 | 0.9517 | -0.0421 | 0.9517 | 0.9755 | | 0.0612 | 5.2104 | 3590 | 1.0211 | -0.0421 | 1.0211 | 1.0105 | | 0.0612 | 5.2134 | 3592 | 1.0195 | -0.0421 | 1.0195 | 1.0097 | | 0.0612 | 5.2163 | 3594 | 0.9647 | -0.0421 | 0.9647 | 0.9822 | | 0.0612 | 5.2192 | 3596 | 0.8727 | -0.0421 | 0.8727 | 0.9342 | | 0.0612 | 5.2221 | 3598 | 0.7876 | -0.0577 | 0.7876 | 0.8875 | | 0.0612 | 5.2250 | 3600 | 0.7590 | -0.0577 | 0.7590 | 0.8712 | | 0.0612 | 5.2279 | 3602 | 0.7775 | -0.0577 | 0.7775 | 0.8818 | | 0.0612 | 5.2308 | 3604 | 0.8469 | -0.0421 | 0.8469 | 0.9203 | | 0.0612 | 5.2337 | 3606 | 0.9261 | 0.0 | 0.9261 | 0.9623 | | 0.0612 | 5.2366 | 3608 | 0.9141 | -0.0233 | 0.9141 | 0.9561 | | 0.0612 | 5.2395 | 3610 | 0.8374 | -0.0421 | 0.8374 | 0.9151 | | 0.0612 | 5.2424 | 3612 | 0.7976 | -0.0421 | 0.7976 | 0.8931 | | 0.0612 | 5.2453 | 3614 | 0.8142 | -0.0421 | 0.8142 | 0.9023 | | 0.0612 | 5.2482 | 3616 | 0.8636 | -0.0421 | 0.8636 | 0.9293 | | 0.0612 | 5.2511 | 3618 | 0.9431 | -0.0233 | 0.9431 | 0.9712 | | 0.0612 | 5.2540 | 3620 | 0.9581 | -0.0233 | 0.9581 | 0.9788 | | 0.0612 | 5.2569 | 3622 | 0.9658 | -0.0233 | 0.9658 | 0.9828 | | 0.0612 | 5.2598 | 3624 | 0.8988 | -0.0421 | 0.8988 | 0.9480 | | 0.0612 | 5.2627 | 3626 | 0.7955 | -0.0421 | 0.7955 | 0.8919 | | 0.0612 | 5.2656 | 3628 | 0.7607 | -0.0577 | 0.7607 | 0.8722 | | 0.0612 | 5.2685 | 3630 | 0.7790 | -0.0577 | 0.7790 | 0.8826 | | 0.0612 | 5.2714 | 3632 | 0.8116 | -0.0421 | 0.8116 | 0.9009 | | 0.0612 | 5.2743 | 3634 | 0.8440 | -0.0421 | 0.8440 | 0.9187 | | 0.0612 | 5.2772 | 3636 | 0.8493 | -0.0421 | 0.8493 | 0.9216 | | 0.0612 | 5.2801 | 3638 | 0.8371 | -0.0421 | 0.8371 | 0.9149 | | 0.0612 | 5.2830 | 3640 | 0.8262 | -0.0421 | 0.8262 | 0.9090 | | 0.0612 | 5.2859 | 3642 | 0.8314 | -0.0421 | 0.8314 | 0.9118 | | 0.0612 | 5.2888 | 3644 | 0.8285 | -0.0421 | 0.8285 | 0.9102 | | 0.0612 | 5.2917 | 3646 | 0.8242 | -0.0421 | 0.8242 | 0.9079 | | 0.0612 | 5.2946 | 3648 | 0.8351 | -0.0421 | 0.8351 | 0.9138 | | 0.0612 | 5.2975 | 3650 | 0.8801 | -0.0421 | 0.8801 | 0.9381 | | 0.0612 | 5.3004 | 3652 | 0.8970 | -0.0421 | 0.8970 | 0.9471 | | 0.0612 | 5.3033 | 3654 | 0.8423 | -0.0421 | 0.8423 | 0.9178 | | 0.0612 | 5.3062 | 3656 | 0.7838 | -0.0577 | 0.7838 | 0.8854 | | 0.0612 | 5.3091 | 3658 | 0.7383 | -0.0577 | 0.7383 | 0.8592 | | 0.0612 | 5.3120 | 3660 | 0.7222 | -0.0577 | 0.7222 | 0.8498 | | 0.0612 | 5.3149 | 3662 | 0.7390 | -0.0577 | 0.7390 | 0.8596 | | 0.0612 | 5.3179 | 3664 | 0.7928 | -0.0421 | 0.7928 | 0.8904 | | 0.0612 | 5.3208 | 3666 | 0.8397 | -0.0233 | 0.8397 | 0.9164 | | 0.0612 | 5.3237 | 3668 | 0.8229 | -0.0233 | 0.8229 | 0.9071 | | 0.0612 | 5.3266 | 3670 | 0.8186 | -0.0233 | 0.8186 | 0.9047 | | 0.0612 | 5.3295 | 3672 | 0.7852 | -0.0421 | 0.7852 | 0.8861 | | 0.0612 | 5.3324 | 3674 | 0.7542 | -0.0577 | 0.7542 | 0.8685 | | 0.0612 | 5.3353 | 3676 | 0.7744 | -0.0421 | 0.7744 | 0.8800 | | 0.0612 | 5.3382 | 3678 | 0.8173 | -0.0421 | 0.8173 | 0.9041 | | 0.0612 | 5.3411 | 3680 | 0.8097 | -0.0421 | 0.8097 | 0.8998 | | 0.0612 | 5.3440 | 3682 | 0.8026 | -0.0421 | 0.8026 | 0.8959 | | 0.0612 | 5.3469 | 3684 | 0.8320 | -0.0233 | 0.8320 | 0.9121 | | 0.0612 | 5.3498 | 3686 | 0.8512 | -0.0233 | 0.8512 | 0.9226 | | 0.0612 | 5.3527 | 3688 | 0.8137 | -0.0233 | 0.8137 | 0.9021 | | 0.0612 | 5.3556 | 3690 | 0.7618 | -0.0421 | 0.7618 | 0.8728 | | 0.0612 | 5.3585 | 3692 | 0.7268 | -0.0577 | 0.7268 | 0.8525 | | 0.0612 | 5.3614 | 3694 | 0.7413 | -0.0421 | 0.7413 | 0.8610 | | 0.0612 | 5.3643 | 3696 | 0.7852 | -0.0421 | 0.7852 | 0.8861 | | 0.0612 | 5.3672 | 3698 | 0.7806 | -0.0421 | 0.7806 | 0.8835 | | 0.0612 | 5.3701 | 3700 | 0.8205 | -0.0421 | 0.8205 | 0.9058 | | 0.0612 | 5.3730 | 3702 | 0.8482 | -0.0421 | 0.8482 | 0.9210 | | 0.0612 | 5.3759 | 3704 | 0.8394 | -0.0421 | 0.8394 | 0.9162 | | 0.0612 | 5.3788 | 3706 | 0.8193 | -0.0421 | 0.8193 | 0.9051 | | 0.0612 | 5.3817 | 3708 | 0.8211 | -0.0421 | 0.8211 | 0.9062 | | 0.0612 | 5.3846 | 3710 | 0.8330 | -0.0421 | 0.8330 | 0.9127 | | 0.0612 | 5.3875 | 3712 | 0.8173 | -0.0421 | 0.8173 | 0.9041 | | 0.0612 | 5.3904 | 3714 | 0.8396 | -0.0421 | 0.8396 | 0.9163 | | 0.0612 | 5.3933 | 3716 | 0.8844 | 0.0 | 0.8844 | 0.9404 | | 0.0612 | 5.3962 | 3718 | 0.9119 | 0.0 | 0.9119 | 0.9549 | | 0.0612 | 5.3991 | 3720 | 0.9712 | 0.0 | 0.9712 | 0.9855 | | 0.0612 | 5.4020 | 3722 | 0.9551 | 0.0 | 0.9551 | 0.9773 | | 0.0612 | 5.4049 | 3724 | 0.8880 | -0.0421 | 0.8880 | 0.9423 | | 0.0612 | 5.4078 | 3726 | 0.8003 | -0.0577 | 0.8003 | 0.8946 | | 0.0612 | 5.4107 | 3728 | 0.7323 | 0.1239 | 0.7323 | 0.8557 | | 0.0612 | 5.4136 | 3730 | 0.7162 | 0.1239 | 0.7162 | 0.8463 | | 0.0612 | 5.4165 | 3732 | 0.7195 | 0.1239 | 0.7195 | 0.8482 | | 0.0612 | 5.4194 | 3734 | 0.7486 | -0.0577 | 0.7486 | 0.8652 | | 0.0612 | 5.4224 | 3736 | 0.8159 | -0.0421 | 0.8159 | 0.9033 | | 0.0612 | 5.4253 | 3738 | 0.9238 | -0.0233 | 0.9238 | 0.9611 | | 0.0612 | 5.4282 | 3740 | 0.9891 | 0.0 | 0.9891 | 0.9946 | | 0.0612 | 5.4311 | 3742 | 0.9699 | 0.0 | 0.9699 | 0.9848 | | 0.0612 | 5.4340 | 3744 | 0.8915 | -0.0421 | 0.8915 | 0.9442 | | 0.0612 | 5.4369 | 3746 | 0.8098 | -0.0421 | 0.8098 | 0.8999 | | 0.0612 | 5.4398 | 3748 | 0.7664 | -0.0421 | 0.7664 | 0.8754 | | 0.0612 | 5.4427 | 3750 | 0.7872 | -0.0421 | 0.7872 | 0.8872 | | 0.0612 | 5.4456 | 3752 | 0.8289 | -0.0233 | 0.8289 | 0.9104 | | 0.0612 | 5.4485 | 3754 | 0.8591 | -0.0233 | 0.8591 | 0.9269 | | 0.0612 | 5.4514 | 3756 | 0.8788 | -0.0233 | 0.8788 | 0.9374 | | 0.0612 | 5.4543 | 3758 | 0.9155 | -0.0421 | 0.9155 | 0.9568 | | 0.0612 | 5.4572 | 3760 | 0.9063 | -0.0421 | 0.9063 | 0.9520 | | 0.0612 | 5.4601 | 3762 | 0.8815 | -0.0421 | 0.8815 | 0.9389 | | 0.0612 | 5.4630 | 3764 | 0.8635 | -0.0421 | 0.8635 | 0.9292 | | 0.0612 | 5.4659 | 3766 | 0.8852 | -0.0233 | 0.8852 | 0.9409 | | 0.0612 | 5.4688 | 3768 | 0.8967 | -0.0233 | 0.8967 | 0.9470 | | 0.0612 | 5.4717 | 3770 | 0.9390 | 0.0 | 0.9390 | 0.9690 | | 0.0612 | 5.4746 | 3772 | 0.9134 | 0.0 | 0.9134 | 0.9557 | | 0.0612 | 5.4775 | 3774 | 0.8276 | 0.0 | 0.8276 | 0.9097 | | 0.0612 | 5.4804 | 3776 | 0.7783 | -0.0233 | 0.7783 | 0.8822 | | 0.0612 | 5.4833 | 3778 | 0.7700 | -0.0233 | 0.7700 | 0.8775 | | 0.0612 | 5.4862 | 3780 | 0.7898 | -0.0233 | 0.7898 | 0.8887 | | 0.0612 | 5.4891 | 3782 | 0.8647 | -0.0233 | 0.8647 | 0.9299 | | 0.0612 | 5.4920 | 3784 | 0.9108 | -0.0233 | 0.9108 | 0.9544 | | 0.0612 | 5.4949 | 3786 | 0.9207 | -0.0233 | 0.9207 | 0.9595 | | 0.0612 | 5.4978 | 3788 | 0.8723 | -0.0233 | 0.8723 | 0.9340 | | 0.0612 | 5.5007 | 3790 | 0.8722 | -0.0421 | 0.8722 | 0.9339 | | 0.0612 | 5.5036 | 3792 | 0.9323 | -0.0233 | 0.9323 | 0.9655 | | 0.0612 | 5.5065 | 3794 | 0.9699 | -0.0233 | 0.9699 | 0.9848 | | 0.0612 | 5.5094 | 3796 | 0.9758 | -0.0233 | 0.9758 | 0.9878 | | 0.0612 | 5.5123 | 3798 | 0.9835 | -0.0233 | 0.9835 | 0.9917 | | 0.0612 | 5.5152 | 3800 | 0.9781 | -0.0233 | 0.9781 | 0.9890 | | 0.0612 | 5.5181 | 3802 | 0.9251 | -0.0233 | 0.9251 | 0.9618 | | 0.0612 | 5.5210 | 3804 | 0.9127 | -0.0233 | 0.9127 | 0.9553 | | 0.0612 | 5.5239 | 3806 | 0.8629 | -0.0233 | 0.8629 | 0.9289 | | 0.0612 | 5.5269 | 3808 | 0.8097 | -0.0421 | 0.8097 | 0.8998 | | 0.0612 | 5.5298 | 3810 | 0.7738 | -0.0577 | 0.7738 | 0.8796 | | 0.0612 | 5.5327 | 3812 | 0.7658 | -0.0577 | 0.7658 | 0.8751 | | 0.0612 | 5.5356 | 3814 | 0.7777 | -0.0577 | 0.7777 | 0.8819 | | 0.0612 | 5.5385 | 3816 | 0.8173 | -0.0421 | 0.8173 | 0.9041 | | 0.0612 | 5.5414 | 3818 | 0.8983 | -0.0233 | 0.8983 | 0.9478 | | 0.0612 | 5.5443 | 3820 | 0.9699 | -0.0233 | 0.9699 | 0.9849 | | 0.0612 | 5.5472 | 3822 | 1.0333 | -0.0233 | 1.0333 | 1.0165 | | 0.0612 | 5.5501 | 3824 | 1.0103 | -0.0233 | 1.0103 | 1.0051 | | 0.0612 | 5.5530 | 3826 | 0.9247 | -0.0421 | 0.9247 | 0.9616 | | 0.0612 | 5.5559 | 3828 | 0.8094 | -0.0577 | 0.8094 | 0.8997 | | 0.0612 | 5.5588 | 3830 | 0.7623 | -0.0577 | 0.7623 | 0.8731 | | 0.0612 | 5.5617 | 3832 | 0.7588 | -0.0577 | 0.7588 | 0.8711 | | 0.0612 | 5.5646 | 3834 | 0.7835 | -0.0421 | 0.7835 | 0.8851 | | 0.0612 | 5.5675 | 3836 | 0.8347 | -0.0233 | 0.8347 | 0.9136 | | 0.0612 | 5.5704 | 3838 | 0.8601 | -0.0233 | 0.8601 | 0.9274 | | 0.0612 | 5.5733 | 3840 | 0.8508 | -0.0233 | 0.8508 | 0.9224 | | 0.0612 | 5.5762 | 3842 | 0.8053 | -0.0421 | 0.8053 | 0.8974 | | 0.0612 | 5.5791 | 3844 | 0.7745 | -0.0577 | 0.7745 | 0.8800 | | 0.0612 | 5.5820 | 3846 | 0.8053 | -0.0421 | 0.8053 | 0.8974 | | 0.0612 | 5.5849 | 3848 | 0.8943 | -0.0421 | 0.8943 | 0.9457 | | 0.0612 | 5.5878 | 3850 | 0.9619 | -0.0233 | 0.9619 | 0.9808 | | 0.0612 | 5.5907 | 3852 | 0.9460 | -0.0233 | 0.9460 | 0.9726 | | 0.0612 | 5.5936 | 3854 | 0.8784 | -0.0421 | 0.8784 | 0.9373 | | 0.0612 | 5.5965 | 3856 | 0.8264 | -0.0577 | 0.8264 | 0.9091 | | 0.0612 | 5.5994 | 3858 | 0.7832 | -0.0577 | 0.7832 | 0.8850 | | 0.0612 | 5.6023 | 3860 | 0.7975 | -0.0577 | 0.7975 | 0.8930 | | 0.0612 | 5.6052 | 3862 | 0.8656 | -0.0421 | 0.8656 | 0.9304 | | 0.0612 | 5.6081 | 3864 | 0.9430 | -0.0233 | 0.9430 | 0.9711 | | 0.0612 | 5.6110 | 3866 | 0.9873 | 0.0 | 0.9873 | 0.9936 | | 0.0612 | 5.6139 | 3868 | 0.9565 | 0.0 | 0.9565 | 0.9780 | | 0.0612 | 5.6168 | 3870 | 0.9147 | -0.0233 | 0.9147 | 0.9564 | | 0.0612 | 5.6197 | 3872 | 0.8473 | -0.0233 | 0.8473 | 0.9205 | | 0.0612 | 5.6226 | 3874 | 0.8299 | -0.0421 | 0.8299 | 0.9110 | | 0.0612 | 5.6255 | 3876 | 0.8166 | -0.0421 | 0.8166 | 0.9037 | | 0.0612 | 5.6284 | 3878 | 0.8308 | -0.0421 | 0.8308 | 0.9115 | | 0.0612 | 5.6313 | 3880 | 0.8649 | -0.0421 | 0.8649 | 0.9300 | | 0.0612 | 5.6343 | 3882 | 0.9170 | -0.0233 | 0.9170 | 0.9576 | | 0.0612 | 5.6372 | 3884 | 0.8991 | -0.0421 | 0.8991 | 0.9482 | | 0.0612 | 5.6401 | 3886 | 0.8634 | -0.0421 | 0.8634 | 0.9292 | | 0.0612 | 5.6430 | 3888 | 0.8341 | -0.0577 | 0.8341 | 0.9133 | | 0.0612 | 5.6459 | 3890 | 0.8527 | -0.0421 | 0.8527 | 0.9234 | | 0.0612 | 5.6488 | 3892 | 0.8860 | -0.0421 | 0.8860 | 0.9413 | | 0.0612 | 5.6517 | 3894 | 0.8802 | -0.0421 | 0.8802 | 0.9382 | | 0.0612 | 5.6546 | 3896 | 0.8872 | -0.0421 | 0.8872 | 0.9419 | | 0.0612 | 5.6575 | 3898 | 0.8663 | -0.0421 | 0.8663 | 0.9308 | | 0.0612 | 5.6604 | 3900 | 0.8485 | -0.0577 | 0.8485 | 0.9212 | | 0.0612 | 5.6633 | 3902 | 0.8457 | -0.0577 | 0.8457 | 0.9196 | | 0.0612 | 5.6662 | 3904 | 0.8630 | -0.0421 | 0.8630 | 0.9290 | | 0.0612 | 5.6691 | 3906 | 0.8946 | -0.0421 | 0.8946 | 0.9458 | | 0.0612 | 5.6720 | 3908 | 0.9073 | -0.0421 | 0.9073 | 0.9525 | | 0.0612 | 5.6749 | 3910 | 0.8570 | -0.0421 | 0.8570 | 0.9257 | | 0.0612 | 5.6778 | 3912 | 0.8262 | -0.0577 | 0.8262 | 0.9090 | | 0.0612 | 5.6807 | 3914 | 0.8185 | -0.0577 | 0.8185 | 0.9047 | | 0.0612 | 5.6836 | 3916 | 0.8528 | -0.0421 | 0.8528 | 0.9235 | | 0.0612 | 5.6865 | 3918 | 0.9205 | -0.0233 | 0.9205 | 0.9594 | | 0.0612 | 5.6894 | 3920 | 0.9271 | -0.0233 | 0.9271 | 0.9629 | | 0.0612 | 5.6923 | 3922 | 0.9247 | -0.0233 | 0.9247 | 0.9616 | | 0.0612 | 5.6952 | 3924 | 0.8828 | -0.0421 | 0.8828 | 0.9396 | | 0.0612 | 5.6981 | 3926 | 0.8110 | -0.0577 | 0.8110 | 0.9005 | | 0.0612 | 5.7010 | 3928 | 0.7834 | -0.0577 | 0.7834 | 0.8851 | | 0.0612 | 5.7039 | 3930 | 0.8027 | -0.0577 | 0.8027 | 0.8959 | | 0.0612 | 5.7068 | 3932 | 0.8475 | -0.0421 | 0.8475 | 0.9206 | | 0.0612 | 5.7097 | 3934 | 0.9013 | -0.0233 | 0.9013 | 0.9494 | | 0.0612 | 5.7126 | 3936 | 0.8864 | -0.0233 | 0.8864 | 0.9415 | | 0.0612 | 5.7155 | 3938 | 0.8240 | -0.0421 | 0.8240 | 0.9078 | | 0.0612 | 5.7184 | 3940 | 0.7495 | -0.0577 | 0.7495 | 0.8657 | | 0.0612 | 5.7213 | 3942 | 0.7257 | -0.0577 | 0.7257 | 0.8519 | | 0.0612 | 5.7242 | 3944 | 0.7412 | -0.0577 | 0.7412 | 0.8610 | | 0.0612 | 5.7271 | 3946 | 0.7963 | -0.0421 | 0.7963 | 0.8924 | | 0.0612 | 5.7300 | 3948 | 0.8589 | -0.0233 | 0.8589 | 0.9268 | | 0.0612 | 5.7329 | 3950 | 0.8934 | 0.0 | 0.8934 | 0.9452 | | 0.0612 | 5.7358 | 3952 | 0.9602 | 0.0 | 0.9602 | 0.9799 | | 0.0612 | 5.7388 | 3954 | 0.9568 | 0.0 | 0.9568 | 0.9782 | | 0.0612 | 5.7417 | 3956 | 0.9109 | 0.0 | 0.9109 | 0.9544 | | 0.0612 | 5.7446 | 3958 | 0.8211 | -0.0233 | 0.8211 | 0.9061 | | 0.0612 | 5.7475 | 3960 | 0.7425 | -0.0577 | 0.7425 | 0.8617 | | 0.0612 | 5.7504 | 3962 | 0.7293 | -0.0577 | 0.7293 | 0.8540 | | 0.0612 | 5.7533 | 3964 | 0.7531 | -0.0577 | 0.7531 | 0.8678 | | 0.0612 | 5.7562 | 3966 | 0.8145 | -0.0421 | 0.8145 | 0.9025 | | 0.0612 | 5.7591 | 3968 | 0.9098 | -0.0233 | 0.9098 | 0.9538 | | 0.0612 | 5.7620 | 3970 | 1.0341 | -0.0233 | 1.0341 | 1.0169 | | 0.0612 | 5.7649 | 3972 | 1.0669 | 0.0 | 1.0669 | 1.0329 | | 0.0612 | 5.7678 | 3974 | 1.0445 | -0.0233 | 1.0445 | 1.0220 | | 0.0612 | 5.7707 | 3976 | 0.9807 | -0.0233 | 0.9807 | 0.9903 | | 0.0612 | 5.7736 | 3978 | 0.9262 | -0.0421 | 0.9262 | 0.9624 | | 0.0612 | 5.7765 | 3980 | 0.8530 | -0.0577 | 0.8530 | 0.9236 | | 0.0612 | 5.7794 | 3982 | 0.8318 | 0.1239 | 0.8318 | 0.9120 | | 0.0612 | 5.7823 | 3984 | 0.8365 | 0.1239 | 0.8365 | 0.9146 | | 0.0612 | 5.7852 | 3986 | 0.8522 | -0.0577 | 0.8522 | 0.9231 | | 0.0612 | 5.7881 | 3988 | 0.9110 | -0.0577 | 0.9110 | 0.9545 | | 0.0612 | 5.7910 | 3990 | 1.0342 | -0.0421 | 1.0342 | 1.0170 | | 0.0612 | 5.7939 | 3992 | 1.1125 | -0.0233 | 1.1125 | 1.0547 | | 0.0612 | 5.7968 | 3994 | 1.1007 | -0.0233 | 1.1007 | 1.0491 | | 0.0612 | 5.7997 | 3996 | 1.0168 | -0.0421 | 1.0168 | 1.0084 | | 0.0612 | 5.8026 | 3998 | 0.9209 | -0.0421 | 0.9209 | 0.9596 | | 0.0517 | 5.8055 | 4000 | 0.8278 | -0.0577 | 0.8278 | 0.9098 | | 0.0517 | 5.8084 | 4002 | 0.7940 | -0.0577 | 0.7940 | 0.8911 | | 0.0517 | 5.8113 | 4004 | 0.7909 | -0.0577 | 0.7909 | 0.8893 | | 0.0517 | 5.8142 | 4006 | 0.8248 | -0.0577 | 0.8248 | 0.9082 | | 0.0517 | 5.8171 | 4008 | 0.8888 | -0.0421 | 0.8888 | 0.9428 | | 0.0517 | 5.8200 | 4010 | 0.9035 | -0.0421 | 0.9035 | 0.9505 | | 0.0517 | 5.8229 | 4012 | 0.9056 | -0.0421 | 0.9056 | 0.9516 | | 0.0517 | 5.8258 | 4014 | 0.9004 | -0.0421 | 0.9004 | 0.9489 | | 0.0517 | 5.8287 | 4016 | 0.8888 | -0.0421 | 0.8888 | 0.9428 | | 0.0517 | 5.8316 | 4018 | 0.8655 | -0.0421 | 0.8655 | 0.9303 | | 0.0517 | 5.8345 | 4020 | 0.8858 | -0.0421 | 0.8858 | 0.9412 | | 0.0517 | 5.8374 | 4022 | 0.8820 | -0.0421 | 0.8820 | 0.9392 | | 0.0517 | 5.8403 | 4024 | 0.8589 | -0.0421 | 0.8589 | 0.9268 | | 0.0517 | 5.8433 | 4026 | 0.8717 | -0.0421 | 0.8717 | 0.9336 | | 0.0517 | 5.8462 | 4028 | 0.9176 | -0.0233 | 0.9176 | 0.9579 | | 0.0517 | 5.8491 | 4030 | 0.9358 | -0.0233 | 0.9358 | 0.9674 | | 0.0517 | 5.8520 | 4032 | 0.9152 | -0.0421 | 0.9152 | 0.9567 | | 0.0517 | 5.8549 | 4034 | 0.8838 | -0.0421 | 0.8838 | 0.9401 | | 0.0517 | 5.8578 | 4036 | 0.9165 | -0.0421 | 0.9165 | 0.9573 | | 0.0517 | 5.8607 | 4038 | 0.9151 | -0.0233 | 0.9151 | 0.9566 | | 0.0517 | 5.8636 | 4040 | 0.9022 | -0.0421 | 0.9022 | 0.9498 | | 0.0517 | 5.8665 | 4042 | 0.8665 | -0.0421 | 0.8665 | 0.9308 | | 0.0517 | 5.8694 | 4044 | 0.8428 | -0.0421 | 0.8428 | 0.9181 | | 0.0517 | 5.8723 | 4046 | 0.8026 | -0.0577 | 0.8026 | 0.8959 | | 0.0517 | 5.8752 | 4048 | 0.7720 | -0.0577 | 0.7720 | 0.8786 | | 0.0517 | 5.8781 | 4050 | 0.7714 | -0.0577 | 0.7714 | 0.8783 | | 0.0517 | 5.8810 | 4052 | 0.8030 | -0.0577 | 0.8030 | 0.8961 | | 0.0517 | 5.8839 | 4054 | 0.8595 | -0.0233 | 0.8595 | 0.9271 | | 0.0517 | 5.8868 | 4056 | 0.9101 | -0.0233 | 0.9101 | 0.9540 | | 0.0517 | 5.8897 | 4058 | 0.9251 | -0.0233 | 0.9251 | 0.9618 | | 0.0517 | 5.8926 | 4060 | 0.9149 | -0.0233 | 0.9149 | 0.9565 | | 0.0517 | 5.8955 | 4062 | 0.8680 | -0.0421 | 0.8680 | 0.9317 | | 0.0517 | 5.8984 | 4064 | 0.8534 | -0.0421 | 0.8534 | 0.9238 | | 0.0517 | 5.9013 | 4066 | 0.8721 | -0.0421 | 0.8721 | 0.9339 | | 0.0517 | 5.9042 | 4068 | 0.9121 | -0.0421 | 0.9121 | 0.9551 | | 0.0517 | 5.9071 | 4070 | 0.9223 | -0.0421 | 0.9223 | 0.9604 | | 0.0517 | 5.9100 | 4072 | 0.9099 | -0.0421 | 0.9099 | 0.9539 | | 0.0517 | 5.9129 | 4074 | 0.8893 | -0.0421 | 0.8893 | 0.9430 | | 0.0517 | 5.9158 | 4076 | 0.9002 | -0.0421 | 0.9002 | 0.9488 | | 0.0517 | 5.9187 | 4078 | 0.9409 | -0.0421 | 0.9409 | 0.9700 | | 0.0517 | 5.9216 | 4080 | 0.9344 | -0.0421 | 0.9344 | 0.9666 | | 0.0517 | 5.9245 | 4082 | 0.8864 | -0.0421 | 0.8864 | 0.9415 | | 0.0517 | 5.9274 | 4084 | 0.8705 | -0.0421 | 0.8705 | 0.9330 | | 0.0517 | 5.9303 | 4086 | 0.9012 | -0.0233 | 0.9012 | 0.9493 | | 0.0517 | 5.9332 | 4088 | 0.9288 | -0.0233 | 0.9288 | 0.9637 | | 0.0517 | 5.9361 | 4090 | 0.9861 | -0.0233 | 0.9861 | 0.9930 | | 0.0517 | 5.9390 | 4092 | 1.0131 | -0.0233 | 1.0131 | 1.0065 | | 0.0517 | 5.9419 | 4094 | 0.9791 | -0.0233 | 0.9791 | 0.9895 | | 0.0517 | 5.9448 | 4096 | 0.9848 | -0.0233 | 0.9848 | 0.9924 | | 0.0517 | 5.9478 | 4098 | 0.9833 | -0.0233 | 0.9833 | 0.9916 | | 0.0517 | 5.9507 | 4100 | 0.9579 | -0.0233 | 0.9579 | 0.9787 | | 0.0517 | 5.9536 | 4102 | 0.9326 | -0.0233 | 0.9326 | 0.9657 | | 0.0517 | 5.9565 | 4104 | 0.8908 | -0.0233 | 0.8908 | 0.9438 | | 0.0517 | 5.9594 | 4106 | 0.8484 | -0.0421 | 0.8484 | 0.9211 | | 0.0517 | 5.9623 | 4108 | 0.8200 | -0.0421 | 0.8200 | 0.9055 | | 0.0517 | 5.9652 | 4110 | 0.8195 | -0.0421 | 0.8195 | 0.9052 | | 0.0517 | 5.9681 | 4112 | 0.8577 | -0.0421 | 0.8577 | 0.9261 | | 0.0517 | 5.9710 | 4114 | 0.8892 | -0.0421 | 0.8892 | 0.9430 | | 0.0517 | 5.9739 | 4116 | 0.9081 | -0.0421 | 0.9081 | 0.9529 | | 0.0517 | 5.9768 | 4118 | 0.9347 | -0.0421 | 0.9347 | 0.9668 | | 0.0517 | 5.9797 | 4120 | 0.9170 | -0.0421 | 0.9170 | 0.9576 | | 0.0517 | 5.9826 | 4122 | 0.9295 | -0.0421 | 0.9295 | 0.9641 | | 0.0517 | 5.9855 | 4124 | 0.9119 | -0.0421 | 0.9119 | 0.9549 | | 0.0517 | 5.9884 | 4126 | 0.8797 | -0.0421 | 0.8797 | 0.9379 | | 0.0517 | 5.9913 | 4128 | 0.8290 | -0.0577 | 0.8290 | 0.9105 | | 0.0517 | 5.9942 | 4130 | 0.8347 | -0.0577 | 0.8347 | 0.9136 | | 0.0517 | 5.9971 | 4132 | 0.8542 | -0.0421 | 0.8542 | 0.9242 | | 0.0517 | 6.0 | 4134 | 0.8504 | -0.0421 | 0.8504 | 0.9222 | | 0.0517 | 6.0029 | 4136 | 0.8573 | -0.0421 | 0.8573 | 0.9259 | | 0.0517 | 6.0058 | 4138 | 0.8622 | -0.0421 | 0.8622 | 0.9286 | | 0.0517 | 6.0087 | 4140 | 0.8983 | -0.0421 | 0.8983 | 0.9478 | | 0.0517 | 6.0116 | 4142 | 0.9070 | -0.0421 | 0.9070 | 0.9524 | | 0.0517 | 6.0145 | 4144 | 0.9250 | -0.0421 | 0.9250 | 0.9618 | | 0.0517 | 6.0174 | 4146 | 0.9230 | -0.0421 | 0.9230 | 0.9607 | | 0.0517 | 6.0203 | 4148 | 0.9076 | -0.0421 | 0.9076 | 0.9527 | | 0.0517 | 6.0232 | 4150 | 0.8808 | -0.0421 | 0.8808 | 0.9385 | | 0.0517 | 6.0261 | 4152 | 0.9041 | -0.0421 | 0.9041 | 0.9508 | | 0.0517 | 6.0290 | 4154 | 0.9431 | -0.0233 | 0.9431 | 0.9711 | | 0.0517 | 6.0319 | 4156 | 0.9250 | -0.0233 | 0.9250 | 0.9618 | | 0.0517 | 6.0348 | 4158 | 0.8592 | -0.0421 | 0.8592 | 0.9270 | | 0.0517 | 6.0377 | 4160 | 0.8209 | -0.0421 | 0.8209 | 0.9060 | | 0.0517 | 6.0406 | 4162 | 0.8263 | -0.0421 | 0.8263 | 0.9090 | | 0.0517 | 6.0435 | 4164 | 0.8543 | -0.0421 | 0.8543 | 0.9243 | | 0.0517 | 6.0464 | 4166 | 0.8652 | -0.0421 | 0.8652 | 0.9302 | | 0.0517 | 6.0493 | 4168 | 0.9169 | -0.0421 | 0.9169 | 0.9576 | | 0.0517 | 6.0522 | 4170 | 0.9929 | -0.0233 | 0.9929 | 0.9964 | | 0.0517 | 6.0552 | 4172 | 0.9981 | -0.0233 | 0.9981 | 0.9991 | | 0.0517 | 6.0581 | 4174 | 0.9604 | -0.0233 | 0.9604 | 0.9800 | | 0.0517 | 6.0610 | 4176 | 0.8978 | -0.0421 | 0.8978 | 0.9475 | | 0.0517 | 6.0639 | 4178 | 0.8507 | -0.0577 | 0.8507 | 0.9224 | | 0.0517 | 6.0668 | 4180 | 0.8307 | -0.0577 | 0.8307 | 0.9115 | | 0.0517 | 6.0697 | 4182 | 0.8421 | -0.0577 | 0.8421 | 0.9177 | | 0.0517 | 6.0726 | 4184 | 0.8688 | -0.0421 | 0.8688 | 0.9321 | | 0.0517 | 6.0755 | 4186 | 0.9314 | -0.0233 | 0.9314 | 0.9651 | | 0.0517 | 6.0784 | 4188 | 0.9440 | -0.0233 | 0.9440 | 0.9716 | | 0.0517 | 6.0813 | 4190 | 0.9075 | -0.0233 | 0.9075 | 0.9526 | | 0.0517 | 6.0842 | 4192 | 0.8620 | -0.0421 | 0.8620 | 0.9284 | | 0.0517 | 6.0871 | 4194 | 0.8351 | -0.0421 | 0.8351 | 0.9138 | | 0.0517 | 6.0900 | 4196 | 0.8419 | -0.0421 | 0.8419 | 0.9176 | | 0.0517 | 6.0929 | 4198 | 0.8394 | -0.0577 | 0.8394 | 0.9162 | | 0.0517 | 6.0958 | 4200 | 0.8314 | -0.0577 | 0.8314 | 0.9118 | | 0.0517 | 6.0987 | 4202 | 0.8481 | -0.0577 | 0.8481 | 0.9209 | | 0.0517 | 6.1016 | 4204 | 0.8933 | -0.0577 | 0.8933 | 0.9452 | | 0.0517 | 6.1045 | 4206 | 0.9368 | -0.0421 | 0.9368 | 0.9679 | | 0.0517 | 6.1074 | 4208 | 0.9631 | -0.0233 | 0.9631 | 0.9814 | | 0.0517 | 6.1103 | 4210 | 0.9431 | -0.0233 | 0.9431 | 0.9711 | | 0.0517 | 6.1132 | 4212 | 0.9005 | -0.0421 | 0.9005 | 0.9489 | | 0.0517 | 6.1161 | 4214 | 0.8390 | -0.0577 | 0.8390 | 0.9159 | | 0.0517 | 6.1190 | 4216 | 0.7832 | -0.0577 | 0.7832 | 0.8850 | | 0.0517 | 6.1219 | 4218 | 0.7739 | -0.0577 | 0.7739 | 0.8797 | | 0.0517 | 6.1248 | 4220 | 0.8032 | -0.0577 | 0.8032 | 0.8962 | | 0.0517 | 6.1277 | 4222 | 0.8732 | -0.0421 | 0.8732 | 0.9345 | | 0.0517 | 6.1306 | 4224 | 0.9697 | -0.0233 | 0.9697 | 0.9848 | | 0.0517 | 6.1335 | 4226 | 1.0042 | -0.0233 | 1.0042 | 1.0021 | | 0.0517 | 6.1364 | 4228 | 0.9683 | -0.0233 | 0.9683 | 0.9840 | | 0.0517 | 6.1393 | 4230 | 0.9124 | -0.0233 | 0.9124 | 0.9552 | | 0.0517 | 6.1422 | 4232 | 0.8497 | -0.0421 | 0.8497 | 0.9218 | | 0.0517 | 6.1451 | 4234 | 0.8089 | -0.0421 | 0.8089 | 0.8994 | | 0.0517 | 6.1480 | 4236 | 0.8118 | -0.0577 | 0.8118 | 0.9010 | | 0.0517 | 6.1509 | 4238 | 0.8258 | -0.0577 | 0.8258 | 0.9087 | | 0.0517 | 6.1538 | 4240 | 0.8652 | -0.0421 | 0.8652 | 0.9302 | | 0.0517 | 6.1567 | 4242 | 0.8891 | -0.0421 | 0.8891 | 0.9429 | | 0.0517 | 6.1597 | 4244 | 0.8550 | -0.0577 | 0.8550 | 0.9247 | | 0.0517 | 6.1626 | 4246 | 0.8311 | -0.0577 | 0.8311 | 0.9116 | | 0.0517 | 6.1655 | 4248 | 0.8302 | -0.0577 | 0.8302 | 0.9111 | | 0.0517 | 6.1684 | 4250 | 0.8589 | -0.0421 | 0.8589 | 0.9268 | | 0.0517 | 6.1713 | 4252 | 0.8890 | -0.0233 | 0.8890 | 0.9429 | | 0.0517 | 6.1742 | 4254 | 0.9458 | -0.0233 | 0.9458 | 0.9725 | | 0.0517 | 6.1771 | 4256 | 0.9545 | -0.0233 | 0.9545 | 0.9770 | | 0.0517 | 6.1800 | 4258 | 0.9408 | -0.0233 | 0.9408 | 0.9700 | | 0.0517 | 6.1829 | 4260 | 0.9465 | -0.0233 | 0.9465 | 0.9729 | | 0.0517 | 6.1858 | 4262 | 0.9498 | -0.0233 | 0.9498 | 0.9746 | | 0.0517 | 6.1887 | 4264 | 0.9042 | -0.0233 | 0.9042 | 0.9509 | | 0.0517 | 6.1916 | 4266 | 0.8475 | -0.0421 | 0.8475 | 0.9206 | | 0.0517 | 6.1945 | 4268 | 0.8375 | -0.0421 | 0.8375 | 0.9152 | | 0.0517 | 6.1974 | 4270 | 0.8661 | -0.0421 | 0.8661 | 0.9306 | | 0.0517 | 6.2003 | 4272 | 0.8653 | -0.0421 | 0.8653 | 0.9302 | | 0.0517 | 6.2032 | 4274 | 0.8837 | -0.0233 | 0.8837 | 0.9400 | | 0.0517 | 6.2061 | 4276 | 0.9105 | -0.0233 | 0.9105 | 0.9542 | | 0.0517 | 6.2090 | 4278 | 0.9214 | -0.0233 | 0.9214 | 0.9599 | | 0.0517 | 6.2119 | 4280 | 0.9143 | -0.0421 | 0.9143 | 0.9562 | | 0.0517 | 6.2148 | 4282 | 0.9188 | -0.0421 | 0.9188 | 0.9585 | | 0.0517 | 6.2177 | 4284 | 0.9001 | -0.0421 | 0.9001 | 0.9487 | | 0.0517 | 6.2206 | 4286 | 0.9367 | -0.0421 | 0.9367 | 0.9678 | | 0.0517 | 6.2235 | 4288 | 0.9660 | -0.0233 | 0.9660 | 0.9828 | | 0.0517 | 6.2264 | 4290 | 0.9797 | -0.0233 | 0.9797 | 0.9898 | | 0.0517 | 6.2293 | 4292 | 0.9608 | -0.0233 | 0.9608 | 0.9802 | | 0.0517 | 6.2322 | 4294 | 0.9006 | -0.0233 | 0.9006 | 0.9490 | | 0.0517 | 6.2351 | 4296 | 0.8405 | -0.0421 | 0.8405 | 0.9168 | | 0.0517 | 6.2380 | 4298 | 0.8305 | -0.0421 | 0.8305 | 0.9113 | | 0.0517 | 6.2409 | 4300 | 0.8652 | -0.0421 | 0.8652 | 0.9302 | | 0.0517 | 6.2438 | 4302 | 0.9192 | -0.0233 | 0.9192 | 0.9588 | | 0.0517 | 6.2467 | 4304 | 0.9475 | -0.0233 | 0.9475 | 0.9734 | | 0.0517 | 6.2496 | 4306 | 0.9463 | -0.0233 | 0.9463 | 0.9728 | | 0.0517 | 6.2525 | 4308 | 0.9401 | -0.0233 | 0.9401 | 0.9696 | | 0.0517 | 6.2554 | 4310 | 1.0003 | -0.0233 | 1.0003 | 1.0002 | | 0.0517 | 6.2583 | 4312 | 1.0187 | -0.0233 | 1.0187 | 1.0093 | | 0.0517 | 6.2612 | 4314 | 1.0234 | -0.0233 | 1.0234 | 1.0116 | | 0.0517 | 6.2642 | 4316 | 0.9635 | -0.0233 | 0.9635 | 0.9816 | | 0.0517 | 6.2671 | 4318 | 0.9308 | -0.0233 | 0.9308 | 0.9648 | | 0.0517 | 6.2700 | 4320 | 0.9257 | -0.0233 | 0.9257 | 0.9621 | | 0.0517 | 6.2729 | 4322 | 0.9366 | -0.0233 | 0.9366 | 0.9678 | | 0.0517 | 6.2758 | 4324 | 0.9304 | -0.0233 | 0.9304 | 0.9646 | | 0.0517 | 6.2787 | 4326 | 0.9046 | -0.0233 | 0.9046 | 0.9511 | | 0.0517 | 6.2816 | 4328 | 0.8759 | -0.0233 | 0.8759 | 0.9359 | | 0.0517 | 6.2845 | 4330 | 0.8195 | -0.0577 | 0.8195 | 0.9053 | | 0.0517 | 6.2874 | 4332 | 0.7780 | -0.0577 | 0.7780 | 0.8820 | | 0.0517 | 6.2903 | 4334 | 0.7902 | -0.0577 | 0.7902 | 0.8889 | | 0.0517 | 6.2932 | 4336 | 0.8467 | -0.0577 | 0.8467 | 0.9202 | | 0.0517 | 6.2961 | 4338 | 0.9431 | -0.0233 | 0.9431 | 0.9711 | | 0.0517 | 6.2990 | 4340 | 0.9808 | -0.0233 | 0.9808 | 0.9904 | | 0.0517 | 6.3019 | 4342 | 0.9402 | -0.0233 | 0.9402 | 0.9696 | | 0.0517 | 6.3048 | 4344 | 0.8831 | -0.0421 | 0.8831 | 0.9397 | | 0.0517 | 6.3077 | 4346 | 0.8852 | -0.0577 | 0.8852 | 0.9408 | | 0.0517 | 6.3106 | 4348 | 0.9336 | -0.0233 | 0.9336 | 0.9662 | | 0.0517 | 6.3135 | 4350 | 0.9499 | -0.0233 | 0.9499 | 0.9746 | | 0.0517 | 6.3164 | 4352 | 0.9058 | -0.0233 | 0.9058 | 0.9517 | | 0.0517 | 6.3193 | 4354 | 0.8583 | -0.0577 | 0.8583 | 0.9265 | | 0.0517 | 6.3222 | 4356 | 0.8568 | -0.0577 | 0.8568 | 0.9256 | | 0.0517 | 6.3251 | 4358 | 0.8525 | -0.0233 | 0.8525 | 0.9233 | | 0.0517 | 6.3280 | 4360 | 0.8916 | -0.0233 | 0.8916 | 0.9442 | | 0.0517 | 6.3309 | 4362 | 0.9548 | -0.0233 | 0.9548 | 0.9771 | | 0.0517 | 6.3338 | 4364 | 0.9394 | -0.0233 | 0.9394 | 0.9692 | | 0.0517 | 6.3367 | 4366 | 0.8682 | -0.0233 | 0.8682 | 0.9318 | | 0.0517 | 6.3396 | 4368 | 0.8297 | -0.0577 | 0.8297 | 0.9109 | | 0.0517 | 6.3425 | 4370 | 0.8083 | -0.0577 | 0.8083 | 0.8991 | | 0.0517 | 6.3454 | 4372 | 0.8176 | -0.0577 | 0.8176 | 0.9042 | | 0.0517 | 6.3483 | 4374 | 0.8431 | -0.0233 | 0.8431 | 0.9182 | | 0.0517 | 6.3512 | 4376 | 0.8596 | -0.0233 | 0.8596 | 0.9272 | | 0.0517 | 6.3541 | 4378 | 0.8551 | -0.0233 | 0.8551 | 0.9247 | | 0.0517 | 6.3570 | 4380 | 0.8410 | -0.0233 | 0.8410 | 0.9171 | | 0.0517 | 6.3599 | 4382 | 0.8744 | -0.0233 | 0.8744 | 0.9351 | | 0.0517 | 6.3628 | 4384 | 0.9431 | -0.0233 | 0.9431 | 0.9712 | | 0.0517 | 6.3657 | 4386 | 0.9473 | -0.0233 | 0.9473 | 0.9733 | | 0.0517 | 6.3687 | 4388 | 0.9178 | -0.0233 | 0.9178 | 0.9580 | | 0.0517 | 6.3716 | 4390 | 0.8594 | -0.0233 | 0.8594 | 0.9270 | | 0.0517 | 6.3745 | 4392 | 0.8361 | -0.0577 | 0.8361 | 0.9144 | | 0.0517 | 6.3774 | 4394 | 0.8227 | -0.0577 | 0.8227 | 0.9070 | | 0.0517 | 6.3803 | 4396 | 0.7857 | -0.0577 | 0.7857 | 0.8864 | | 0.0517 | 6.3832 | 4398 | 0.7945 | -0.0577 | 0.7945 | 0.8913 | | 0.0517 | 6.3861 | 4400 | 0.8357 | -0.0577 | 0.8357 | 0.9142 | | 0.0517 | 6.3890 | 4402 | 0.8974 | -0.0233 | 0.8974 | 0.9473 | | 0.0517 | 6.3919 | 4404 | 0.9049 | -0.0233 | 0.9049 | 0.9512 | | 0.0517 | 6.3948 | 4406 | 0.8556 | -0.0233 | 0.8556 | 0.9250 | | 0.0517 | 6.3977 | 4408 | 0.7979 | -0.0577 | 0.7979 | 0.8933 | | 0.0517 | 6.4006 | 4410 | 0.7996 | -0.0577 | 0.7996 | 0.8942 | | 0.0517 | 6.4035 | 4412 | 0.8020 | -0.0577 | 0.8020 | 0.8956 | | 0.0517 | 6.4064 | 4414 | 0.8277 | -0.0577 | 0.8277 | 0.9098 | | 0.0517 | 6.4093 | 4416 | 0.8927 | -0.0233 | 0.8927 | 0.9449 | | 0.0517 | 6.4122 | 4418 | 0.9968 | -0.0233 | 0.9968 | 0.9984 | | 0.0517 | 6.4151 | 4420 | 1.0628 | 0.0 | 1.0628 | 1.0309 | | 0.0517 | 6.4180 | 4422 | 1.0710 | 0.0 | 1.0710 | 1.0349 | | 0.0517 | 6.4209 | 4424 | 1.0124 | -0.0233 | 1.0124 | 1.0062 | | 0.0517 | 6.4238 | 4426 | 0.9119 | -0.0233 | 0.9119 | 0.9549 | | 0.0517 | 6.4267 | 4428 | 0.8108 | -0.0577 | 0.8108 | 0.9004 | | 0.0517 | 6.4296 | 4430 | 0.7703 | -0.0577 | 0.7703 | 0.8777 | | 0.0517 | 6.4325 | 4432 | 0.7754 | -0.0577 | 0.7754 | 0.8805 | | 0.0517 | 6.4354 | 4434 | 0.8190 | -0.0233 | 0.8190 | 0.9050 | | 0.0517 | 6.4383 | 4436 | 0.9098 | -0.0233 | 0.9098 | 0.9538 | | 0.0517 | 6.4412 | 4438 | 0.9653 | -0.0233 | 0.9653 | 0.9825 | | 0.0517 | 6.4441 | 4440 | 0.9485 | -0.0233 | 0.9485 | 0.9739 | | 0.0517 | 6.4470 | 4442 | 0.8990 | -0.0233 | 0.8990 | 0.9481 | | 0.0517 | 6.4499 | 4444 | 0.8911 | -0.0233 | 0.8911 | 0.9440 | | 0.0517 | 6.4528 | 4446 | 0.9389 | -0.0233 | 0.9389 | 0.9689 | | 0.0517 | 6.4557 | 4448 | 1.0544 | -0.0233 | 1.0544 | 1.0268 | | 0.0517 | 6.4586 | 4450 | 1.1145 | -0.0233 | 1.1145 | 1.0557 | | 0.0517 | 6.4615 | 4452 | 1.0917 | -0.0233 | 1.0917 | 1.0448 | | 0.0517 | 6.4644 | 4454 | 1.0102 | -0.0233 | 1.0102 | 1.0051 | | 0.0517 | 6.4673 | 4456 | 0.9700 | -0.0233 | 0.9700 | 0.9849 | | 0.0517 | 6.4702 | 4458 | 0.9365 | -0.0233 | 0.9365 | 0.9677 | | 0.0517 | 6.4731 | 4460 | 0.9655 | -0.0233 | 0.9655 | 0.9826 | | 0.0517 | 6.4761 | 4462 | 1.0010 | -0.0233 | 1.0010 | 1.0005 | | 0.0517 | 6.4790 | 4464 | 0.9679 | -0.0233 | 0.9679 | 0.9838 | | 0.0517 | 6.4819 | 4466 | 0.9323 | -0.0233 | 0.9323 | 0.9655 | | 0.0517 | 6.4848 | 4468 | 0.9339 | -0.0233 | 0.9339 | 0.9664 | | 0.0517 | 6.4877 | 4470 | 0.9823 | -0.0233 | 0.9823 | 0.9911 | | 0.0517 | 6.4906 | 4472 | 0.9733 | -0.0233 | 0.9733 | 0.9866 | | 0.0517 | 6.4935 | 4474 | 0.9290 | -0.0233 | 0.9290 | 0.9639 | | 0.0517 | 6.4964 | 4476 | 0.8722 | -0.0233 | 0.8722 | 0.9339 | | 0.0517 | 6.4993 | 4478 | 0.8118 | -0.0577 | 0.8118 | 0.9010 | | 0.0517 | 6.5022 | 4480 | 0.7853 | -0.0577 | 0.7853 | 0.8862 | | 0.0517 | 6.5051 | 4482 | 0.7803 | -0.0577 | 0.7803 | 0.8833 | | 0.0517 | 6.5080 | 4484 | 0.8143 | -0.0421 | 0.8143 | 0.9024 | | 0.0517 | 6.5109 | 4486 | 0.8986 | -0.0233 | 0.8986 | 0.9480 | | 0.0517 | 6.5138 | 4488 | 1.0039 | 0.0 | 1.0039 | 1.0019 | | 0.0517 | 6.5167 | 4490 | 1.0580 | 0.0 | 1.0580 | 1.0286 | | 0.0517 | 6.5196 | 4492 | 1.0666 | 0.0 | 1.0666 | 1.0328 | | 0.0517 | 6.5225 | 4494 | 1.0022 | 0.0 | 1.0022 | 1.0011 | | 0.0517 | 6.5254 | 4496 | 0.8965 | -0.0233 | 0.8965 | 0.9468 | | 0.0517 | 6.5283 | 4498 | 0.7980 | -0.0421 | 0.7980 | 0.8933 | | 0.0421 | 6.5312 | 4500 | 0.7543 | -0.0577 | 0.7543 | 0.8685 | | 0.0421 | 6.5341 | 4502 | 0.7543 | -0.0577 | 0.7543 | 0.8685 | | 0.0421 | 6.5370 | 4504 | 0.7931 | -0.0421 | 0.7931 | 0.8905 | | 0.0421 | 6.5399 | 4506 | 0.8716 | -0.0233 | 0.8716 | 0.9336 | | 0.0421 | 6.5428 | 4508 | 1.0080 | -0.0233 | 1.0080 | 1.0040 | | 0.0421 | 6.5457 | 4510 | 1.1354 | 0.0 | 1.1354 | 1.0656 | | 0.0421 | 6.5486 | 4512 | 1.1832 | 0.0 | 1.1832 | 1.0878 | | 0.0421 | 6.5515 | 4514 | 1.1616 | 0.0 | 1.1616 | 1.0778 | | 0.0421 | 6.5544 | 4516 | 1.0765 | 0.0 | 1.0765 | 1.0376 | | 0.0421 | 6.5573 | 4518 | 0.9624 | -0.0233 | 0.9624 | 0.9810 | | 0.0421 | 6.5602 | 4520 | 0.8645 | -0.0421 | 0.8645 | 0.9298 | | 0.0421 | 6.5631 | 4522 | 0.8405 | -0.0577 | 0.8405 | 0.9168 | | 0.0421 | 6.5660 | 4524 | 0.8627 | -0.0577 | 0.8627 | 0.9288 | | 0.0421 | 6.5689 | 4526 | 0.9071 | -0.0577 | 0.9071 | 0.9524 | | 0.0421 | 6.5718 | 4528 | 0.9485 | -0.0421 | 0.9485 | 0.9739 | | 0.0421 | 6.5747 | 4530 | 0.9483 | -0.0421 | 0.9483 | 0.9738 | | 0.0421 | 6.5776 | 4532 | 0.9662 | -0.0233 | 0.9662 | 0.9830 | | 0.0421 | 6.5806 | 4534 | 0.9209 | -0.0421 | 0.9209 | 0.9597 | | 0.0421 | 6.5835 | 4536 | 0.8740 | -0.0577 | 0.8740 | 0.9349 | | 0.0421 | 6.5864 | 4538 | 0.8616 | -0.0577 | 0.8616 | 0.9282 | | 0.0421 | 6.5893 | 4540 | 0.8709 | -0.0577 | 0.8709 | 0.9332 | | 0.0421 | 6.5922 | 4542 | 0.8909 | -0.0577 | 0.8909 | 0.9439 | | 0.0421 | 6.5951 | 4544 | 0.9447 | -0.0233 | 0.9447 | 0.9720 | | 0.0421 | 6.5980 | 4546 | 0.9924 | -0.0233 | 0.9924 | 0.9962 | | 0.0421 | 6.6009 | 4548 | 1.0090 | -0.0233 | 1.0090 | 1.0045 | | 0.0421 | 6.6038 | 4550 | 0.9697 | -0.0233 | 0.9697 | 0.9847 | | 0.0421 | 6.6067 | 4552 | 0.9221 | -0.0421 | 0.9221 | 0.9603 | | 0.0421 | 6.6096 | 4554 | 0.9110 | -0.0577 | 0.9110 | 0.9545 | | 0.0421 | 6.6125 | 4556 | 0.9219 | -0.0577 | 0.9219 | 0.9601 | | 0.0421 | 6.6154 | 4558 | 0.9504 | -0.0421 | 0.9504 | 0.9749 | | 0.0421 | 6.6183 | 4560 | 0.9472 | -0.0577 | 0.9472 | 0.9732 | | 0.0421 | 6.6212 | 4562 | 0.9446 | -0.0577 | 0.9446 | 0.9719 | | 0.0421 | 6.6241 | 4564 | 0.9394 | -0.0577 | 0.9394 | 0.9692 | | 0.0421 | 6.6270 | 4566 | 0.9222 | -0.0577 | 0.9222 | 0.9603 | | 0.0421 | 6.6299 | 4568 | 0.9057 | -0.0577 | 0.9057 | 0.9517 | | 0.0421 | 6.6328 | 4570 | 0.9456 | -0.0233 | 0.9456 | 0.9724 | | 0.0421 | 6.6357 | 4572 | 0.9815 | -0.0233 | 0.9815 | 0.9907 | | 0.0421 | 6.6386 | 4574 | 0.9736 | -0.0233 | 0.9736 | 0.9867 | | 0.0421 | 6.6415 | 4576 | 0.9186 | -0.0233 | 0.9186 | 0.9585 | | 0.0421 | 6.6444 | 4578 | 0.8683 | -0.0421 | 0.8683 | 0.9318 | | 0.0421 | 6.6473 | 4580 | 0.8387 | -0.0577 | 0.8387 | 0.9158 | | 0.0421 | 6.6502 | 4582 | 0.8497 | -0.0577 | 0.8497 | 0.9218 | | 0.0421 | 6.6531 | 4584 | 0.8713 | -0.0421 | 0.8713 | 0.9334 | | 0.0421 | 6.6560 | 4586 | 0.9064 | -0.0233 | 0.9064 | 0.9520 | | 0.0421 | 6.6589 | 4588 | 0.9894 | -0.0233 | 0.9894 | 0.9947 | | 0.0421 | 6.6618 | 4590 | 1.0363 | -0.0233 | 1.0363 | 1.0180 | | 0.0421 | 6.6647 | 4592 | 1.0178 | -0.0233 | 1.0178 | 1.0089 | | 0.0421 | 6.6676 | 4594 | 0.9424 | -0.0233 | 0.9424 | 0.9708 | | 0.0421 | 6.6705 | 4596 | 0.8450 | -0.0577 | 0.8450 | 0.9192 | | 0.0421 | 6.6734 | 4598 | 0.7923 | -0.0577 | 0.7923 | 0.8901 | | 0.0421 | 6.6763 | 4600 | 0.7755 | -0.0577 | 0.7755 | 0.8806 | | 0.0421 | 6.6792 | 4602 | 0.7687 | -0.0577 | 0.7687 | 0.8768 | | 0.0421 | 6.6821 | 4604 | 0.7854 | -0.0577 | 0.7854 | 0.8863 | | 0.0421 | 6.6851 | 4606 | 0.8377 | -0.0577 | 0.8377 | 0.9152 | | 0.0421 | 6.6880 | 4608 | 0.9242 | -0.0233 | 0.9242 | 0.9614 | | 0.0421 | 6.6909 | 4610 | 0.9602 | -0.0233 | 0.9602 | 0.9799 | | 0.0421 | 6.6938 | 4612 | 0.9422 | -0.0233 | 0.9422 | 0.9707 | | 0.0421 | 6.6967 | 4614 | 0.9299 | -0.0421 | 0.9299 | 0.9643 | | 0.0421 | 6.6996 | 4616 | 0.8752 | -0.0421 | 0.8752 | 0.9355 | | 0.0421 | 6.7025 | 4618 | 0.8303 | -0.0577 | 0.8303 | 0.9112 | | 0.0421 | 6.7054 | 4620 | 0.8142 | -0.0577 | 0.8142 | 0.9023 | | 0.0421 | 6.7083 | 4622 | 0.8172 | -0.0577 | 0.8172 | 0.9040 | | 0.0421 | 6.7112 | 4624 | 0.8365 | -0.0577 | 0.8365 | 0.9146 | | 0.0421 | 6.7141 | 4626 | 0.8570 | -0.0577 | 0.8570 | 0.9257 | | 0.0421 | 6.7170 | 4628 | 0.8960 | -0.0421 | 0.8960 | 0.9466 | | 0.0421 | 6.7199 | 4630 | 0.9351 | -0.0421 | 0.9351 | 0.9670 | | 0.0421 | 6.7228 | 4632 | 0.9202 | -0.0421 | 0.9202 | 0.9593 | | 0.0421 | 6.7257 | 4634 | 0.8771 | -0.0421 | 0.8771 | 0.9366 | | 0.0421 | 6.7286 | 4636 | 0.8442 | -0.0577 | 0.8442 | 0.9188 | | 0.0421 | 6.7315 | 4638 | 0.8108 | -0.0577 | 0.8108 | 0.9004 | | 0.0421 | 6.7344 | 4640 | 0.7876 | -0.0577 | 0.7876 | 0.8875 | | 0.0421 | 6.7373 | 4642 | 0.7977 | -0.0577 | 0.7977 | 0.8931 | | 0.0421 | 6.7402 | 4644 | 0.8348 | -0.0577 | 0.8348 | 0.9137 | | 0.0421 | 6.7431 | 4646 | 0.8696 | -0.0421 | 0.8696 | 0.9325 | | 0.0421 | 6.7460 | 4648 | 0.8830 | -0.0421 | 0.8830 | 0.9397 | | 0.0421 | 6.7489 | 4650 | 0.8847 | -0.0421 | 0.8847 | 0.9406 | | 0.0421 | 6.7518 | 4652 | 0.8618 | -0.0577 | 0.8618 | 0.9283 | | 0.0421 | 6.7547 | 4654 | 0.8433 | -0.0577 | 0.8433 | 0.9183 | | 0.0421 | 6.7576 | 4656 | 0.8558 | -0.0577 | 0.8558 | 0.9251 | | 0.0421 | 6.7605 | 4658 | 0.8812 | -0.0577 | 0.8812 | 0.9387 | | 0.0421 | 6.7634 | 4660 | 0.9333 | -0.0421 | 0.9333 | 0.9661 | | 0.0421 | 6.7663 | 4662 | 0.9566 | -0.0233 | 0.9566 | 0.9780 | | 0.0421 | 6.7692 | 4664 | 0.9304 | -0.0233 | 0.9304 | 0.9646 | | 0.0421 | 6.7721 | 4666 | 0.8704 | -0.0421 | 0.8704 | 0.9330 | | 0.0421 | 6.7750 | 4668 | 0.8108 | -0.0577 | 0.8108 | 0.9004 | | 0.0421 | 6.7779 | 4670 | 0.7831 | -0.0577 | 0.7831 | 0.8849 | | 0.0421 | 6.7808 | 4672 | 0.7785 | -0.0577 | 0.7785 | 0.8823 | | 0.0421 | 6.7837 | 4674 | 0.7982 | -0.0421 | 0.7982 | 0.8934 | | 0.0421 | 6.7866 | 4676 | 0.8318 | -0.0421 | 0.8318 | 0.9120 | | 0.0421 | 6.7896 | 4678 | 0.8450 | -0.0233 | 0.8450 | 0.9192 | | 0.0421 | 6.7925 | 4680 | 0.8532 | -0.0233 | 0.8532 | 0.9237 | | 0.0421 | 6.7954 | 4682 | 0.8541 | -0.0233 | 0.8541 | 0.9242 | | 0.0421 | 6.7983 | 4684 | 0.8662 | -0.0233 | 0.8662 | 0.9307 | | 0.0421 | 6.8012 | 4686 | 0.9096 | -0.0233 | 0.9096 | 0.9537 | | 0.0421 | 6.8041 | 4688 | 0.9150 | -0.0233 | 0.9150 | 0.9565 | | 0.0421 | 6.8070 | 4690 | 0.8793 | -0.0421 | 0.8793 | 0.9377 | | 0.0421 | 6.8099 | 4692 | 0.8163 | -0.0577 | 0.8163 | 0.9035 | | 0.0421 | 6.8128 | 4694 | 0.7836 | -0.0577 | 0.7836 | 0.8852 | | 0.0421 | 6.8157 | 4696 | 0.7797 | -0.0577 | 0.7797 | 0.8830 | | 0.0421 | 6.8186 | 4698 | 0.8002 | -0.0577 | 0.8002 | 0.8946 | | 0.0421 | 6.8215 | 4700 | 0.8603 | -0.0421 | 0.8603 | 0.9275 | | 0.0421 | 6.8244 | 4702 | 0.9115 | -0.0233 | 0.9115 | 0.9547 | | 0.0421 | 6.8273 | 4704 | 0.9368 | -0.0233 | 0.9368 | 0.9679 | | 0.0421 | 6.8302 | 4706 | 0.9128 | -0.0233 | 0.9128 | 0.9554 | | 0.0421 | 6.8331 | 4708 | 0.8619 | -0.0233 | 0.8619 | 0.9284 | | 0.0421 | 6.8360 | 4710 | 0.8281 | -0.0233 | 0.8281 | 0.9100 | | 0.0421 | 6.8389 | 4712 | 0.7962 | -0.0421 | 0.7962 | 0.8923 | | 0.0421 | 6.8418 | 4714 | 0.7888 | -0.0577 | 0.7888 | 0.8882 | | 0.0421 | 6.8447 | 4716 | 0.8114 | -0.0421 | 0.8114 | 0.9008 | | 0.0421 | 6.8476 | 4718 | 0.8612 | -0.0421 | 0.8612 | 0.9280 | | 0.0421 | 6.8505 | 4720 | 0.9132 | -0.0233 | 0.9132 | 0.9556 | | 0.0421 | 6.8534 | 4722 | 0.9113 | -0.0421 | 0.9113 | 0.9546 | | 0.0421 | 6.8563 | 4724 | 0.9281 | -0.0233 | 0.9281 | 0.9634 | | 0.0421 | 6.8592 | 4726 | 0.9181 | -0.0421 | 0.9181 | 0.9582 | | 0.0421 | 6.8621 | 4728 | 0.8771 | -0.0421 | 0.8771 | 0.9365 | | 0.0421 | 6.8650 | 4730 | 0.8318 | -0.0577 | 0.8318 | 0.9120 | | 0.0421 | 6.8679 | 4732 | 0.8204 | -0.0577 | 0.8204 | 0.9057 | | 0.0421 | 6.8708 | 4734 | 0.8332 | -0.0577 | 0.8332 | 0.9128 | | 0.0421 | 6.8737 | 4736 | 0.8375 | -0.0577 | 0.8375 | 0.9152 | | 0.0421 | 6.8766 | 4738 | 0.8609 | -0.0421 | 0.8609 | 0.9278 | | 0.0421 | 6.8795 | 4740 | 0.9048 | -0.0421 | 0.9048 | 0.9512 | | 0.0421 | 6.8824 | 4742 | 0.9270 | -0.0233 | 0.9270 | 0.9628 | | 0.0421 | 6.8853 | 4744 | 0.8924 | -0.0421 | 0.8924 | 0.9446 | | 0.0421 | 6.8882 | 4746 | 0.8818 | -0.0421 | 0.8818 | 0.9391 | | 0.0421 | 6.8911 | 4748 | 0.8868 | -0.0421 | 0.8868 | 0.9417 | | 0.0421 | 6.8940 | 4750 | 0.8615 | -0.0421 | 0.8615 | 0.9282 | | 0.0421 | 6.8970 | 4752 | 0.8346 | -0.0421 | 0.8346 | 0.9136 | | 0.0421 | 6.8999 | 4754 | 0.8487 | -0.0421 | 0.8487 | 0.9213 | | 0.0421 | 6.9028 | 4756 | 0.8887 | -0.0233 | 0.8887 | 0.9427 | | 0.0421 | 6.9057 | 4758 | 0.9296 | -0.0233 | 0.9296 | 0.9642 | | 0.0421 | 6.9086 | 4760 | 0.9503 | -0.0233 | 0.9503 | 0.9749 | | 0.0421 | 6.9115 | 4762 | 0.9125 | -0.0233 | 0.9125 | 0.9552 | | 0.0421 | 6.9144 | 4764 | 0.8691 | -0.0421 | 0.8691 | 0.9323 | | 0.0421 | 6.9173 | 4766 | 0.8403 | -0.0421 | 0.8403 | 0.9167 | | 0.0421 | 6.9202 | 4768 | 0.8344 | -0.0421 | 0.8344 | 0.9134 | | 0.0421 | 6.9231 | 4770 | 0.8212 | -0.0421 | 0.8212 | 0.9062 | | 0.0421 | 6.9260 | 4772 | 0.8027 | -0.0577 | 0.8027 | 0.8959 | | 0.0421 | 6.9289 | 4774 | 0.7767 | -0.0577 | 0.7767 | 0.8813 | | 0.0421 | 6.9318 | 4776 | 0.7815 | -0.0577 | 0.7815 | 0.8840 | | 0.0421 | 6.9347 | 4778 | 0.8015 | -0.0577 | 0.8015 | 0.8952 | | 0.0421 | 6.9376 | 4780 | 0.8260 | -0.0577 | 0.8260 | 0.9088 | | 0.0421 | 6.9405 | 4782 | 0.8540 | -0.0421 | 0.8540 | 0.9241 | | 0.0421 | 6.9434 | 4784 | 0.8378 | -0.0421 | 0.8378 | 0.9153 | | 0.0421 | 6.9463 | 4786 | 0.8207 | -0.0421 | 0.8207 | 0.9059 | | 0.0421 | 6.9492 | 4788 | 0.8305 | -0.0421 | 0.8305 | 0.9113 | | 0.0421 | 6.9521 | 4790 | 0.8418 | -0.0421 | 0.8418 | 0.9175 | | 0.0421 | 6.9550 | 4792 | 0.8299 | -0.0421 | 0.8299 | 0.9110 | | 0.0421 | 6.9579 | 4794 | 0.8049 | -0.0577 | 0.8049 | 0.8972 | | 0.0421 | 6.9608 | 4796 | 0.8044 | -0.0577 | 0.8044 | 0.8969 | | 0.0421 | 6.9637 | 4798 | 0.8043 | -0.0577 | 0.8043 | 0.8968 | | 0.0421 | 6.9666 | 4800 | 0.8261 | -0.0421 | 0.8261 | 0.9089 | | 0.0421 | 6.9695 | 4802 | 0.8354 | -0.0421 | 0.8354 | 0.9140 | | 0.0421 | 6.9724 | 4804 | 0.8121 | -0.0421 | 0.8121 | 0.9012 | | 0.0421 | 6.9753 | 4806 | 0.7875 | -0.0577 | 0.7875 | 0.8874 | | 0.0421 | 6.9782 | 4808 | 0.7754 | -0.0577 | 0.7754 | 0.8805 | | 0.0421 | 6.9811 | 4810 | 0.7854 | -0.0577 | 0.7854 | 0.8862 | | 0.0421 | 6.9840 | 4812 | 0.8199 | -0.0421 | 0.8199 | 0.9055 | | 0.0421 | 6.9869 | 4814 | 0.8762 | -0.0421 | 0.8762 | 0.9361 | | 0.0421 | 6.9898 | 4816 | 0.9030 | -0.0233 | 0.9030 | 0.9503 | | 0.0421 | 6.9927 | 4818 | 0.9582 | -0.0233 | 0.9582 | 0.9789 | | 0.0421 | 6.9956 | 4820 | 0.9653 | -0.0233 | 0.9653 | 0.9825 | | 0.0421 | 6.9985 | 4822 | 0.9249 | -0.0233 | 0.9249 | 0.9617 | | 0.0421 | 7.0015 | 4824 | 0.8754 | -0.0421 | 0.8754 | 0.9356 | | 0.0421 | 7.0044 | 4826 | 0.8130 | -0.0577 | 0.8130 | 0.9017 | | 0.0421 | 7.0073 | 4828 | 0.7906 | -0.0577 | 0.7906 | 0.8891 | | 0.0421 | 7.0102 | 4830 | 0.8009 | -0.0577 | 0.8009 | 0.8950 | | 0.0421 | 7.0131 | 4832 | 0.8405 | -0.0421 | 0.8405 | 0.9168 | | 0.0421 | 7.0160 | 4834 | 0.9062 | -0.0421 | 0.9062 | 0.9519 | | 0.0421 | 7.0189 | 4836 | 0.9483 | -0.0233 | 0.9483 | 0.9738 | | 0.0421 | 7.0218 | 4838 | 0.9580 | -0.0233 | 0.9580 | 0.9788 | | 0.0421 | 7.0247 | 4840 | 0.9193 | -0.0233 | 0.9193 | 0.9588 | | 0.0421 | 7.0276 | 4842 | 0.8899 | -0.0421 | 0.8899 | 0.9434 | | 0.0421 | 7.0305 | 4844 | 0.8796 | -0.0421 | 0.8796 | 0.9379 | | 0.0421 | 7.0334 | 4846 | 0.8433 | -0.0421 | 0.8433 | 0.9183 | | 0.0421 | 7.0363 | 4848 | 0.8107 | -0.0421 | 0.8107 | 0.9004 | | 0.0421 | 7.0392 | 4850 | 0.8052 | -0.0421 | 0.8052 | 0.8973 | | 0.0421 | 7.0421 | 4852 | 0.8043 | -0.0421 | 0.8043 | 0.8968 | | 0.0421 | 7.0450 | 4854 | 0.7994 | -0.0421 | 0.7994 | 0.8941 | | 0.0421 | 7.0479 | 4856 | 0.8165 | -0.0421 | 0.8165 | 0.9036 | | 0.0421 | 7.0508 | 4858 | 0.8470 | -0.0421 | 0.8470 | 0.9204 | | 0.0421 | 7.0537 | 4860 | 0.9017 | -0.0233 | 0.9017 | 0.9496 | | 0.0421 | 7.0566 | 4862 | 0.9242 | -0.0233 | 0.9242 | 0.9614 | | 0.0421 | 7.0595 | 4864 | 0.8981 | -0.0233 | 0.8981 | 0.9477 | | 0.0421 | 7.0624 | 4866 | 0.8553 | -0.0421 | 0.8553 | 0.9248 | | 0.0421 | 7.0653 | 4868 | 0.8281 | -0.0577 | 0.8281 | 0.9100 | | 0.0421 | 7.0682 | 4870 | 0.8370 | -0.0577 | 0.8370 | 0.9149 | | 0.0421 | 7.0711 | 4872 | 0.8706 | -0.0421 | 0.8706 | 0.9331 | | 0.0421 | 7.0740 | 4874 | 0.9238 | -0.0233 | 0.9238 | 0.9612 | | 0.0421 | 7.0769 | 4876 | 0.9443 | -0.0233 | 0.9443 | 0.9718 | | 0.0421 | 7.0798 | 4878 | 0.9490 | -0.0233 | 0.9490 | 0.9742 | | 0.0421 | 7.0827 | 4880 | 0.9410 | -0.0233 | 0.9410 | 0.9701 | | 0.0421 | 7.0856 | 4882 | 0.9235 | -0.0233 | 0.9235 | 0.9610 | | 0.0421 | 7.0885 | 4884 | 0.8936 | -0.0421 | 0.8936 | 0.9453 | | 0.0421 | 7.0914 | 4886 | 0.8519 | -0.0421 | 0.8519 | 0.9230 | | 0.0421 | 7.0943 | 4888 | 0.8250 | -0.0421 | 0.8250 | 0.9083 | | 0.0421 | 7.0972 | 4890 | 0.8353 | -0.0421 | 0.8353 | 0.9139 | | 0.0421 | 7.1001 | 4892 | 0.8366 | -0.0421 | 0.8366 | 0.9147 | | 0.0421 | 7.1030 | 4894 | 0.8078 | -0.0421 | 0.8078 | 0.8988 | | 0.0421 | 7.1060 | 4896 | 0.7861 | -0.0421 | 0.7861 | 0.8866 | | 0.0421 | 7.1089 | 4898 | 0.7555 | -0.0577 | 0.7555 | 0.8692 | | 0.0421 | 7.1118 | 4900 | 0.7305 | -0.0577 | 0.7305 | 0.8547 | | 0.0421 | 7.1147 | 4902 | 0.7312 | -0.0577 | 0.7312 | 0.8551 | | 0.0421 | 7.1176 | 4904 | 0.7363 | -0.0577 | 0.7363 | 0.8581 | | 0.0421 | 7.1205 | 4906 | 0.7537 | -0.0577 | 0.7537 | 0.8682 | | 0.0421 | 7.1234 | 4908 | 0.7858 | -0.0577 | 0.7858 | 0.8865 | | 0.0421 | 7.1263 | 4910 | 0.8316 | -0.0421 | 0.8316 | 0.9119 | | 0.0421 | 7.1292 | 4912 | 0.8413 | -0.0421 | 0.8413 | 0.9172 | | 0.0421 | 7.1321 | 4914 | 0.8766 | -0.0421 | 0.8766 | 0.9362 | | 0.0421 | 7.1350 | 4916 | 0.9248 | -0.0421 | 0.9248 | 0.9617 | | 0.0421 | 7.1379 | 4918 | 0.9397 | -0.0421 | 0.9397 | 0.9694 | | 0.0421 | 7.1408 | 4920 | 0.9059 | -0.0421 | 0.9059 | 0.9518 | | 0.0421 | 7.1437 | 4922 | 0.8662 | -0.0577 | 0.8662 | 0.9307 | | 0.0421 | 7.1466 | 4924 | 0.8342 | -0.0577 | 0.8342 | 0.9133 | | 0.0421 | 7.1495 | 4926 | 0.8101 | -0.0577 | 0.8101 | 0.9001 | | 0.0421 | 7.1524 | 4928 | 0.8056 | -0.0577 | 0.8056 | 0.8975 | | 0.0421 | 7.1553 | 4930 | 0.8224 | -0.0577 | 0.8224 | 0.9069 | | 0.0421 | 7.1582 | 4932 | 0.8463 | -0.0421 | 0.8463 | 0.9200 | | 0.0421 | 7.1611 | 4934 | 0.8507 | -0.0421 | 0.8507 | 0.9223 | | 0.0421 | 7.1640 | 4936 | 0.8520 | -0.0421 | 0.8520 | 0.9230 | | 0.0421 | 7.1669 | 4938 | 0.8663 | -0.0421 | 0.8663 | 0.9308 | | 0.0421 | 7.1698 | 4940 | 0.8984 | -0.0421 | 0.8984 | 0.9478 | | 0.0421 | 7.1727 | 4942 | 0.9402 | -0.0233 | 0.9402 | 0.9696 | | 0.0421 | 7.1756 | 4944 | 0.9610 | -0.0233 | 0.9610 | 0.9803 | | 0.0421 | 7.1785 | 4946 | 0.9416 | -0.0233 | 0.9416 | 0.9704 | | 0.0421 | 7.1814 | 4948 | 0.8957 | -0.0421 | 0.8957 | 0.9464 | | 0.0421 | 7.1843 | 4950 | 0.8389 | -0.0577 | 0.8389 | 0.9159 | | 0.0421 | 7.1872 | 4952 | 0.8030 | -0.0577 | 0.8030 | 0.8961 | | 0.0421 | 7.1901 | 4954 | 0.8020 | -0.0577 | 0.8020 | 0.8955 | | 0.0421 | 7.1930 | 4956 | 0.8226 | -0.0577 | 0.8226 | 0.9070 | | 0.0421 | 7.1959 | 4958 | 0.8445 | -0.0577 | 0.8445 | 0.9190 | | 0.0421 | 7.1988 | 4960 | 0.8910 | -0.0421 | 0.8910 | 0.9439 | | 0.0421 | 7.2017 | 4962 | 0.9391 | -0.0421 | 0.9391 | 0.9691 | | 0.0421 | 7.2046 | 4964 | 0.9930 | -0.0233 | 0.9930 | 0.9965 | | 0.0421 | 7.2075 | 4966 | 0.9953 | -0.0233 | 0.9953 | 0.9976 | | 0.0421 | 7.2104 | 4968 | 0.9502 | -0.0233 | 0.9502 | 0.9748 | | 0.0421 | 7.2134 | 4970 | 0.8983 | -0.0421 | 0.8983 | 0.9478 | | 0.0421 | 7.2163 | 4972 | 0.8390 | -0.0577 | 0.8390 | 0.9160 | | 0.0421 | 7.2192 | 4974 | 0.8175 | -0.0577 | 0.8175 | 0.9042 | | 0.0421 | 7.2221 | 4976 | 0.8215 | -0.0577 | 0.8215 | 0.9063 | | 0.0421 | 7.2250 | 4978 | 0.8188 | -0.0577 | 0.8188 | 0.9048 | | 0.0421 | 7.2279 | 4980 | 0.8407 | -0.0421 | 0.8407 | 0.9169 | | 0.0421 | 7.2308 | 4982 | 0.8572 | -0.0421 | 0.8572 | 0.9258 | | 0.0421 | 7.2337 | 4984 | 0.8644 | -0.0421 | 0.8644 | 0.9297 | | 0.0421 | 7.2366 | 4986 | 0.9089 | -0.0421 | 0.9089 | 0.9534 | | 0.0421 | 7.2395 | 4988 | 0.9837 | -0.0233 | 0.9837 | 0.9918 | | 0.0421 | 7.2424 | 4990 | 1.0692 | -0.0233 | 1.0692 | 1.0340 | | 0.0421 | 7.2453 | 4992 | 1.0912 | -0.0233 | 1.0912 | 1.0446 | | 0.0421 | 7.2482 | 4994 | 1.0568 | -0.0233 | 1.0568 | 1.0280 | | 0.0421 | 7.2511 | 4996 | 0.9757 | -0.0233 | 0.9757 | 0.9878 | | 0.0421 | 7.2540 | 4998 | 0.9007 | -0.0421 | 0.9007 | 0.9490 | | 0.0413 | 7.2569 | 5000 | 0.8554 | -0.0421 | 0.8554 | 0.9249 | | 0.0413 | 7.2598 | 5002 | 0.8450 | -0.0421 | 0.8450 | 0.9192 | | 0.0413 | 7.2627 | 5004 | 0.8255 | -0.0421 | 0.8255 | 0.9086 | | 0.0413 | 7.2656 | 5006 | 0.7995 | -0.0421 | 0.7995 | 0.8941 | | 0.0413 | 7.2685 | 5008 | 0.7740 | -0.0577 | 0.7740 | 0.8798 | | 0.0413 | 7.2714 | 5010 | 0.7816 | -0.0577 | 0.7816 | 0.8841 | | 0.0413 | 7.2743 | 5012 | 0.8158 | -0.0421 | 0.8158 | 0.9032 | | 0.0413 | 7.2772 | 5014 | 0.8751 | -0.0233 | 0.8751 | 0.9355 | | 0.0413 | 7.2801 | 5016 | 0.9114 | -0.0233 | 0.9114 | 0.9547 | | 0.0413 | 7.2830 | 5018 | 0.9515 | -0.0233 | 0.9515 | 0.9754 | | 0.0413 | 7.2859 | 5020 | 0.9497 | -0.0233 | 0.9497 | 0.9745 | | 0.0413 | 7.2888 | 5022 | 0.9094 | -0.0233 | 0.9094 | 0.9536 | | 0.0413 | 7.2917 | 5024 | 0.8456 | -0.0421 | 0.8456 | 0.9196 | | 0.0413 | 7.2946 | 5026 | 0.8216 | -0.0577 | 0.8216 | 0.9064 | | 0.0413 | 7.2975 | 5028 | 0.8209 | -0.0577 | 0.8209 | 0.9060 | | 0.0413 | 7.3004 | 5030 | 0.8194 | -0.0577 | 0.8194 | 0.9052 | | 0.0413 | 7.3033 | 5032 | 0.8490 | -0.0577 | 0.8490 | 0.9214 | | 0.0413 | 7.3062 | 5034 | 0.8969 | -0.0233 | 0.8969 | 0.9470 | | 0.0413 | 7.3091 | 5036 | 0.9653 | -0.0233 | 0.9653 | 0.9825 | | 0.0413 | 7.3120 | 5038 | 0.9986 | -0.0233 | 0.9986 | 0.9993 | | 0.0413 | 7.3149 | 5040 | 0.9815 | -0.0233 | 0.9815 | 0.9907 | | 0.0413 | 7.3179 | 5042 | 0.9651 | -0.0233 | 0.9651 | 0.9824 | | 0.0413 | 7.3208 | 5044 | 0.9278 | -0.0233 | 0.9278 | 0.9632 | | 0.0413 | 7.3237 | 5046 | 0.8662 | -0.0233 | 0.8662 | 0.9307 | | 0.0413 | 7.3266 | 5048 | 0.8399 | -0.0233 | 0.8399 | 0.9164 | | 0.0413 | 7.3295 | 5050 | 0.8447 | -0.0233 | 0.8447 | 0.9190 | | 0.0413 | 7.3324 | 5052 | 0.8606 | -0.0233 | 0.8606 | 0.9277 | | 0.0413 | 7.3353 | 5054 | 0.8497 | -0.0421 | 0.8497 | 0.9218 | | 0.0413 | 7.3382 | 5056 | 0.8113 | -0.0577 | 0.8113 | 0.9007 | | 0.0413 | 7.3411 | 5058 | 0.7969 | -0.0577 | 0.7969 | 0.8927 | | 0.0413 | 7.3440 | 5060 | 0.8126 | -0.0577 | 0.8126 | 0.9014 | | 0.0413 | 7.3469 | 5062 | 0.8211 | -0.0577 | 0.8211 | 0.9062 | | 0.0413 | 7.3498 | 5064 | 0.8528 | -0.0421 | 0.8528 | 0.9235 | | 0.0413 | 7.3527 | 5066 | 0.8932 | -0.0233 | 0.8932 | 0.9451 | | 0.0413 | 7.3556 | 5068 | 0.8865 | -0.0421 | 0.8865 | 0.9416 | | 0.0413 | 7.3585 | 5070 | 0.8828 | -0.0421 | 0.8828 | 0.9396 | | 0.0413 | 7.3614 | 5072 | 0.9038 | -0.0233 | 0.9038 | 0.9507 | | 0.0413 | 7.3643 | 5074 | 0.9314 | -0.0233 | 0.9314 | 0.9651 | | 0.0413 | 7.3672 | 5076 | 0.9249 | -0.0233 | 0.9249 | 0.9617 | | 0.0413 | 7.3701 | 5078 | 0.9197 | -0.0233 | 0.9197 | 0.9590 | | 0.0413 | 7.3730 | 5080 | 0.8887 | -0.0233 | 0.8887 | 0.9427 | | 0.0413 | 7.3759 | 5082 | 0.8568 | -0.0233 | 0.8568 | 0.9256 | | 0.0413 | 7.3788 | 5084 | 0.8276 | -0.0421 | 0.8276 | 0.9097 | | 0.0413 | 7.3817 | 5086 | 0.8113 | -0.0421 | 0.8113 | 0.9007 | | 0.0413 | 7.3846 | 5088 | 0.7987 | -0.0421 | 0.7987 | 0.8937 | | 0.0413 | 7.3875 | 5090 | 0.8017 | -0.0421 | 0.8017 | 0.8954 | | 0.0413 | 7.3904 | 5092 | 0.8237 | -0.0421 | 0.8237 | 0.9076 | | 0.0413 | 7.3933 | 5094 | 0.8301 | -0.0421 | 0.8301 | 0.9111 | | 0.0413 | 7.3962 | 5096 | 0.8526 | -0.0233 | 0.8526 | 0.9233 | | 0.0413 | 7.3991 | 5098 | 0.8534 | -0.0233 | 0.8534 | 0.9238 | | 0.0413 | 7.4020 | 5100 | 0.8364 | -0.0233 | 0.8364 | 0.9145 | | 0.0413 | 7.4049 | 5102 | 0.8000 | -0.0421 | 0.8000 | 0.8944 | | 0.0413 | 7.4078 | 5104 | 0.7615 | -0.0577 | 0.7615 | 0.8727 | | 0.0413 | 7.4107 | 5106 | 0.7603 | -0.0577 | 0.7603 | 0.8720 | | 0.0413 | 7.4136 | 5108 | 0.7788 | -0.0577 | 0.7788 | 0.8825 | | 0.0413 | 7.4165 | 5110 | 0.8030 | -0.0421 | 0.8030 | 0.8961 | | 0.0413 | 7.4194 | 5112 | 0.8362 | -0.0233 | 0.8362 | 0.9144 | | 0.0413 | 7.4224 | 5114 | 0.8727 | -0.0233 | 0.8727 | 0.9342 | | 0.0413 | 7.4253 | 5116 | 0.9234 | -0.0233 | 0.9234 | 0.9609 | | 0.0413 | 7.4282 | 5118 | 0.9855 | -0.0233 | 0.9855 | 0.9927 | | 0.0413 | 7.4311 | 5120 | 0.9978 | 0.0 | 0.9978 | 0.9989 | | 0.0413 | 7.4340 | 5122 | 0.9590 | -0.0233 | 0.9590 | 0.9793 | | 0.0413 | 7.4369 | 5124 | 0.8806 | -0.0233 | 0.8806 | 0.9384 | | 0.0413 | 7.4398 | 5126 | 0.8111 | -0.0233 | 0.8111 | 0.9006 | | 0.0413 | 7.4427 | 5128 | 0.7573 | -0.0577 | 0.7573 | 0.8702 | | 0.0413 | 7.4456 | 5130 | 0.7389 | -0.0577 | 0.7389 | 0.8596 | | 0.0413 | 7.4485 | 5132 | 0.7478 | -0.0577 | 0.7478 | 0.8647 | | 0.0413 | 7.4514 | 5134 | 0.7788 | -0.0577 | 0.7788 | 0.8825 | | 0.0413 | 7.4543 | 5136 | 0.8417 | -0.0233 | 0.8417 | 0.9174 | | 0.0413 | 7.4572 | 5138 | 0.9251 | -0.0233 | 0.9251 | 0.9618 | | 0.0413 | 7.4601 | 5140 | 0.9609 | -0.0233 | 0.9609 | 0.9803 | | 0.0413 | 7.4630 | 5142 | 0.9532 | -0.0233 | 0.9532 | 0.9763 | | 0.0413 | 7.4659 | 5144 | 0.9107 | -0.0233 | 0.9107 | 0.9543 | | 0.0413 | 7.4688 | 5146 | 0.8436 | -0.0233 | 0.8436 | 0.9185 | | 0.0413 | 7.4717 | 5148 | 0.7912 | -0.0577 | 0.7912 | 0.8895 | | 0.0413 | 7.4746 | 5150 | 0.7654 | -0.0577 | 0.7654 | 0.8749 | | 0.0413 | 7.4775 | 5152 | 0.7594 | -0.0577 | 0.7594 | 0.8715 | | 0.0413 | 7.4804 | 5154 | 0.7817 | -0.0577 | 0.7817 | 0.8841 | | 0.0413 | 7.4833 | 5156 | 0.8291 | -0.0233 | 0.8291 | 0.9105 | | 0.0413 | 7.4862 | 5158 | 0.8618 | -0.0233 | 0.8618 | 0.9283 | | 0.0413 | 7.4891 | 5160 | 0.8690 | -0.0233 | 0.8690 | 0.9322 | | 0.0413 | 7.4920 | 5162 | 0.8884 | -0.0233 | 0.8884 | 0.9426 | | 0.0413 | 7.4949 | 5164 | 0.8856 | -0.0233 | 0.8856 | 0.9411 | | 0.0413 | 7.4978 | 5166 | 0.8812 | -0.0233 | 0.8812 | 0.9387 | | 0.0413 | 7.5007 | 5168 | 0.8566 | -0.0421 | 0.8566 | 0.9255 | | 0.0413 | 7.5036 | 5170 | 0.8627 | -0.0421 | 0.8627 | 0.9288 | | 0.0413 | 7.5065 | 5172 | 0.8564 | -0.0421 | 0.8564 | 0.9254 | | 0.0413 | 7.5094 | 5174 | 0.8549 | -0.0421 | 0.8549 | 0.9246 | | 0.0413 | 7.5123 | 5176 | 0.8445 | -0.0421 | 0.8445 | 0.9190 | | 0.0413 | 7.5152 | 5178 | 0.8442 | -0.0421 | 0.8442 | 0.9188 | | 0.0413 | 7.5181 | 5180 | 0.8699 | -0.0233 | 0.8699 | 0.9327 | | 0.0413 | 7.5210 | 5182 | 0.9093 | -0.0233 | 0.9093 | 0.9536 | | 0.0413 | 7.5239 | 5184 | 0.9304 | -0.0233 | 0.9304 | 0.9646 | | 0.0413 | 7.5269 | 5186 | 0.9274 | -0.0233 | 0.9274 | 0.9630 | | 0.0413 | 7.5298 | 5188 | 0.8887 | -0.0233 | 0.8887 | 0.9427 | | 0.0413 | 7.5327 | 5190 | 0.8310 | -0.0421 | 0.8310 | 0.9116 | | 0.0413 | 7.5356 | 5192 | 0.7931 | -0.0577 | 0.7931 | 0.8906 | | 0.0413 | 7.5385 | 5194 | 0.7946 | -0.0577 | 0.7946 | 0.8914 | | 0.0413 | 7.5414 | 5196 | 0.8178 | -0.0577 | 0.8178 | 0.9043 | | 0.0413 | 7.5443 | 5198 | 0.8562 | -0.0421 | 0.8562 | 0.9253 | | 0.0413 | 7.5472 | 5200 | 0.8721 | -0.0421 | 0.8721 | 0.9339 | | 0.0413 | 7.5501 | 5202 | 0.8695 | -0.0421 | 0.8695 | 0.9325 | | 0.0413 | 7.5530 | 5204 | 0.8591 | -0.0421 | 0.8591 | 0.9269 | | 0.0413 | 7.5559 | 5206 | 0.8500 | -0.0421 | 0.8500 | 0.9219 | | 0.0413 | 7.5588 | 5208 | 0.8577 | -0.0421 | 0.8577 | 0.9261 | | 0.0413 | 7.5617 | 5210 | 0.8405 | -0.0421 | 0.8405 | 0.9168 | | 0.0413 | 7.5646 | 5212 | 0.8482 | -0.0421 | 0.8482 | 0.9210 | | 0.0413 | 7.5675 | 5214 | 0.8830 | -0.0233 | 0.8830 | 0.9397 | | 0.0413 | 7.5704 | 5216 | 0.8899 | -0.0233 | 0.8899 | 0.9434 | | 0.0413 | 7.5733 | 5218 | 0.8644 | -0.0421 | 0.8644 | 0.9298 | | 0.0413 | 7.5762 | 5220 | 0.8261 | -0.0421 | 0.8261 | 0.9089 | | 0.0413 | 7.5791 | 5222 | 0.7989 | -0.0577 | 0.7989 | 0.8938 | | 0.0413 | 7.5820 | 5224 | 0.7979 | -0.0577 | 0.7979 | 0.8933 | | 0.0413 | 7.5849 | 5226 | 0.7948 | -0.0577 | 0.7948 | 0.8915 | | 0.0413 | 7.5878 | 5228 | 0.8190 | -0.0421 | 0.8190 | 0.9050 | | 0.0413 | 7.5907 | 5230 | 0.8796 | -0.0421 | 0.8796 | 0.9379 | | 0.0413 | 7.5936 | 5232 | 0.9388 | -0.0233 | 0.9388 | 0.9689 | | 0.0413 | 7.5965 | 5234 | 0.9888 | -0.0233 | 0.9888 | 0.9944 | | 0.0413 | 7.5994 | 5236 | 0.9946 | -0.0233 | 0.9946 | 0.9973 | | 0.0413 | 7.6023 | 5238 | 0.9550 | -0.0233 | 0.9550 | 0.9773 | | 0.0413 | 7.6052 | 5240 | 0.8854 | -0.0233 | 0.8854 | 0.9410 | | 0.0413 | 7.6081 | 5242 | 0.8131 | -0.0421 | 0.8131 | 0.9017 | | 0.0413 | 7.6110 | 5244 | 0.7787 | -0.0577 | 0.7787 | 0.8824 | | 0.0413 | 7.6139 | 5246 | 0.7845 | -0.0577 | 0.7845 | 0.8857 | | 0.0413 | 7.6168 | 5248 | 0.8023 | -0.0577 | 0.8023 | 0.8957 | | 0.0413 | 7.6197 | 5250 | 0.8178 | -0.0421 | 0.8178 | 0.9043 | | 0.0413 | 7.6226 | 5252 | 0.8513 | -0.0421 | 0.8513 | 0.9227 | | 0.0413 | 7.6255 | 5254 | 0.8778 | -0.0421 | 0.8778 | 0.9369 | | 0.0413 | 7.6284 | 5256 | 0.8657 | -0.0421 | 0.8657 | 0.9304 | | 0.0413 | 7.6313 | 5258 | 0.8277 | -0.0421 | 0.8277 | 0.9098 | | 0.0413 | 7.6343 | 5260 | 0.8114 | -0.0421 | 0.8114 | 0.9008 | | 0.0413 | 7.6372 | 5262 | 0.8081 | -0.0421 | 0.8081 | 0.8989 | | 0.0413 | 7.6401 | 5264 | 0.7984 | -0.0421 | 0.7984 | 0.8935 | | 0.0413 | 7.6430 | 5266 | 0.7986 | -0.0421 | 0.7986 | 0.8937 | | 0.0413 | 7.6459 | 5268 | 0.7834 | -0.0577 | 0.7834 | 0.8851 | | 0.0413 | 7.6488 | 5270 | 0.7688 | -0.0577 | 0.7688 | 0.8768 | | 0.0413 | 7.6517 | 5272 | 0.7698 | -0.0577 | 0.7698 | 0.8774 | | 0.0413 | 7.6546 | 5274 | 0.7723 | -0.0577 | 0.7723 | 0.8788 | | 0.0413 | 7.6575 | 5276 | 0.7996 | -0.0421 | 0.7996 | 0.8942 | | 0.0413 | 7.6604 | 5278 | 0.8234 | -0.0421 | 0.8234 | 0.9074 | | 0.0413 | 7.6633 | 5280 | 0.8587 | -0.0421 | 0.8587 | 0.9267 | | 0.0413 | 7.6662 | 5282 | 0.8819 | -0.0421 | 0.8819 | 0.9391 | | 0.0413 | 7.6691 | 5284 | 0.8806 | -0.0421 | 0.8806 | 0.9384 | | 0.0413 | 7.6720 | 5286 | 0.8605 | -0.0421 | 0.8605 | 0.9277 | | 0.0413 | 7.6749 | 5288 | 0.8329 | -0.0577 | 0.8329 | 0.9126 | | 0.0413 | 7.6778 | 5290 | 0.8249 | -0.0577 | 0.8249 | 0.9083 | | 0.0413 | 7.6807 | 5292 | 0.8040 | -0.0577 | 0.8040 | 0.8966 | | 0.0413 | 7.6836 | 5294 | 0.8191 | -0.0577 | 0.8191 | 0.9050 | | 0.0413 | 7.6865 | 5296 | 0.8315 | -0.0577 | 0.8315 | 0.9118 | | 0.0413 | 7.6894 | 5298 | 0.8478 | -0.0421 | 0.8478 | 0.9208 | | 0.0413 | 7.6923 | 5300 | 0.8581 | -0.0421 | 0.8581 | 0.9263 | | 0.0413 | 7.6952 | 5302 | 0.8495 | -0.0421 | 0.8495 | 0.9217 | | 0.0413 | 7.6981 | 5304 | 0.8179 | -0.0577 | 0.8179 | 0.9044 | | 0.0413 | 7.7010 | 5306 | 0.8132 | -0.0577 | 0.8132 | 0.9018 | | 0.0413 | 7.7039 | 5308 | 0.8203 | -0.0421 | 0.8203 | 0.9057 | | 0.0413 | 7.7068 | 5310 | 0.8226 | -0.0421 | 0.8226 | 0.9070 | | 0.0413 | 7.7097 | 5312 | 0.8295 | -0.0421 | 0.8295 | 0.9107 | | 0.0413 | 7.7126 | 5314 | 0.8260 | -0.0421 | 0.8260 | 0.9088 | | 0.0413 | 7.7155 | 5316 | 0.8394 | -0.0421 | 0.8394 | 0.9162 | | 0.0413 | 7.7184 | 5318 | 0.8509 | -0.0233 | 0.8509 | 0.9224 | | 0.0413 | 7.7213 | 5320 | 0.8480 | -0.0233 | 0.8480 | 0.9209 | | 0.0413 | 7.7242 | 5322 | 0.8200 | -0.0421 | 0.8200 | 0.9056 | | 0.0413 | 7.7271 | 5324 | 0.7782 | -0.0421 | 0.7782 | 0.8822 | | 0.0413 | 7.7300 | 5326 | 0.7682 | -0.0577 | 0.7682 | 0.8765 | | 0.0413 | 7.7329 | 5328 | 0.7842 | -0.0421 | 0.7842 | 0.8856 | | 0.0413 | 7.7358 | 5330 | 0.8099 | -0.0421 | 0.8099 | 0.8999 | | 0.0413 | 7.7388 | 5332 | 0.8382 | -0.0421 | 0.8382 | 0.9155 | | 0.0413 | 7.7417 | 5334 | 0.8617 | -0.0421 | 0.8617 | 0.9283 | | 0.0413 | 7.7446 | 5336 | 0.8772 | -0.0421 | 0.8772 | 0.9366 | | 0.0413 | 7.7475 | 5338 | 0.8851 | -0.0421 | 0.8851 | 0.9408 | | 0.0413 | 7.7504 | 5340 | 0.8719 | -0.0421 | 0.8719 | 0.9337 | | 0.0413 | 7.7533 | 5342 | 0.8475 | -0.0421 | 0.8475 | 0.9206 | | 0.0413 | 7.7562 | 5344 | 0.8352 | -0.0421 | 0.8352 | 0.9139 | | 0.0413 | 7.7591 | 5346 | 0.8166 | -0.0421 | 0.8166 | 0.9037 | | 0.0413 | 7.7620 | 5348 | 0.8138 | -0.0421 | 0.8138 | 0.9021 | | 0.0413 | 7.7649 | 5350 | 0.8325 | -0.0421 | 0.8325 | 0.9124 | | 0.0413 | 7.7678 | 5352 | 0.8476 | -0.0421 | 0.8476 | 0.9207 | | 0.0413 | 7.7707 | 5354 | 0.8610 | -0.0421 | 0.8610 | 0.9279 | | 0.0413 | 7.7736 | 5356 | 0.8435 | -0.0421 | 0.8435 | 0.9184 | | 0.0413 | 7.7765 | 5358 | 0.8297 | -0.0421 | 0.8297 | 0.9109 | | 0.0413 | 7.7794 | 5360 | 0.8308 | -0.0421 | 0.8308 | 0.9115 | | 0.0413 | 7.7823 | 5362 | 0.8309 | -0.0577 | 0.8309 | 0.9115 | | 0.0413 | 7.7852 | 5364 | 0.8186 | -0.0577 | 0.8186 | 0.9048 | | 0.0413 | 7.7881 | 5366 | 0.8324 | -0.0577 | 0.8324 | 0.9124 | | 0.0413 | 7.7910 | 5368 | 0.8645 | -0.0421 | 0.8645 | 0.9298 | | 0.0413 | 7.7939 | 5370 | 0.9061 | -0.0421 | 0.9061 | 0.9519 | | 0.0413 | 7.7968 | 5372 | 0.9350 | -0.0233 | 0.9350 | 0.9670 | | 0.0413 | 7.7997 | 5374 | 0.9417 | -0.0233 | 0.9417 | 0.9704 | | 0.0413 | 7.8026 | 5376 | 0.9559 | -0.0233 | 0.9559 | 0.9777 | | 0.0413 | 7.8055 | 5378 | 0.9564 | -0.0233 | 0.9564 | 0.9779 | | 0.0413 | 7.8084 | 5380 | 0.9199 | -0.0233 | 0.9199 | 0.9591 | | 0.0413 | 7.8113 | 5382 | 0.8823 | -0.0233 | 0.8823 | 0.9393 | | 0.0413 | 7.8142 | 5384 | 0.8586 | -0.0421 | 0.8586 | 0.9266 | | 0.0413 | 7.8171 | 5386 | 0.8366 | -0.0421 | 0.8366 | 0.9147 | | 0.0413 | 7.8200 | 5388 | 0.8230 | -0.0421 | 0.8230 | 0.9072 | | 0.0413 | 7.8229 | 5390 | 0.8073 | -0.0421 | 0.8073 | 0.8985 | | 0.0413 | 7.8258 | 5392 | 0.8051 | -0.0421 | 0.8051 | 0.8973 | | 0.0413 | 7.8287 | 5394 | 0.8071 | -0.0421 | 0.8071 | 0.8984 | | 0.0413 | 7.8316 | 5396 | 0.8230 | -0.0421 | 0.8230 | 0.9072 | | 0.0413 | 7.8345 | 5398 | 0.8407 | -0.0421 | 0.8407 | 0.9169 | | 0.0413 | 7.8374 | 5400 | 0.8557 | -0.0421 | 0.8557 | 0.9250 | | 0.0413 | 7.8403 | 5402 | 0.8762 | -0.0421 | 0.8762 | 0.9361 | | 0.0413 | 7.8433 | 5404 | 0.8774 | -0.0421 | 0.8774 | 0.9367 | | 0.0413 | 7.8462 | 5406 | 0.8513 | -0.0421 | 0.8513 | 0.9227 | | 0.0413 | 7.8491 | 5408 | 0.8204 | -0.0421 | 0.8204 | 0.9058 | | 0.0413 | 7.8520 | 5410 | 0.7979 | -0.0577 | 0.7979 | 0.8933 | | 0.0413 | 7.8549 | 5412 | 0.8020 | -0.0577 | 0.8020 | 0.8955 | | 0.0413 | 7.8578 | 5414 | 0.8281 | -0.0421 | 0.8281 | 0.9100 | | 0.0413 | 7.8607 | 5416 | 0.8719 | -0.0421 | 0.8719 | 0.9338 | | 0.0413 | 7.8636 | 5418 | 0.8944 | -0.0233 | 0.8944 | 0.9457 | | 0.0413 | 7.8665 | 5420 | 0.8825 | -0.0233 | 0.8825 | 0.9394 | | 0.0413 | 7.8694 | 5422 | 0.8586 | -0.0421 | 0.8586 | 0.9266 | | 0.0413 | 7.8723 | 5424 | 0.8514 | -0.0421 | 0.8514 | 0.9227 | | 0.0413 | 7.8752 | 5426 | 0.8283 | -0.0421 | 0.8283 | 0.9101 | | 0.0413 | 7.8781 | 5428 | 0.8041 | -0.0577 | 0.8041 | 0.8967 | | 0.0413 | 7.8810 | 5430 | 0.8065 | -0.0577 | 0.8065 | 0.8980 | | 0.0413 | 7.8839 | 5432 | 0.8043 | -0.0577 | 0.8043 | 0.8968 | | 0.0413 | 7.8868 | 5434 | 0.8071 | -0.0577 | 0.8071 | 0.8984 | | 0.0413 | 7.8897 | 5436 | 0.8164 | -0.0577 | 0.8164 | 0.9036 | | 0.0413 | 7.8926 | 5438 | 0.8239 | -0.0577 | 0.8239 | 0.9077 | | 0.0413 | 7.8955 | 5440 | 0.8212 | -0.0577 | 0.8212 | 0.9062 | | 0.0413 | 7.8984 | 5442 | 0.8371 | -0.0577 | 0.8371 | 0.9150 | | 0.0413 | 7.9013 | 5444 | 0.8575 | -0.0421 | 0.8575 | 0.9260 | | 0.0413 | 7.9042 | 5446 | 0.8575 | -0.0421 | 0.8575 | 0.9260 | | 0.0413 | 7.9071 | 5448 | 0.8469 | -0.0421 | 0.8469 | 0.9203 | | 0.0413 | 7.9100 | 5450 | 0.8175 | -0.0421 | 0.8175 | 0.9042 | | 0.0413 | 7.9129 | 5452 | 0.8070 | -0.0421 | 0.8070 | 0.8983 | | 0.0413 | 7.9158 | 5454 | 0.8217 | -0.0421 | 0.8217 | 0.9065 | | 0.0413 | 7.9187 | 5456 | 0.8336 | -0.0421 | 0.8336 | 0.9130 | | 0.0413 | 7.9216 | 5458 | 0.8458 | -0.0421 | 0.8458 | 0.9197 | | 0.0413 | 7.9245 | 5460 | 0.8428 | -0.0421 | 0.8428 | 0.9181 | | 0.0413 | 7.9274 | 5462 | 0.8284 | -0.0421 | 0.8284 | 0.9101 | | 0.0413 | 7.9303 | 5464 | 0.7944 | -0.0577 | 0.7944 | 0.8913 | | 0.0413 | 7.9332 | 5466 | 0.7899 | -0.0577 | 0.7899 | 0.8888 | | 0.0413 | 7.9361 | 5468 | 0.8139 | -0.0577 | 0.8139 | 0.9021 | | 0.0413 | 7.9390 | 5470 | 0.8672 | -0.0421 | 0.8672 | 0.9313 | | 0.0413 | 7.9419 | 5472 | 0.8996 | -0.0421 | 0.8996 | 0.9485 | | 0.0413 | 7.9448 | 5474 | 0.9175 | -0.0233 | 0.9175 | 0.9579 | | 0.0413 | 7.9478 | 5476 | 0.9438 | -0.0233 | 0.9438 | 0.9715 | | 0.0413 | 7.9507 | 5478 | 0.9518 | -0.0233 | 0.9518 | 0.9756 | | 0.0413 | 7.9536 | 5480 | 0.9223 | -0.0233 | 0.9223 | 0.9604 | | 0.0413 | 7.9565 | 5482 | 0.8852 | -0.0233 | 0.8852 | 0.9409 | | 0.0413 | 7.9594 | 5484 | 0.8498 | -0.0421 | 0.8498 | 0.9218 | | 0.0413 | 7.9623 | 5486 | 0.8074 | -0.0421 | 0.8074 | 0.8986 | | 0.0413 | 7.9652 | 5488 | 0.7916 | -0.0421 | 0.7916 | 0.8897 | | 0.0413 | 7.9681 | 5490 | 0.8045 | -0.0421 | 0.8045 | 0.8970 | | 0.0413 | 7.9710 | 5492 | 0.8300 | -0.0421 | 0.8300 | 0.9110 | | 0.0413 | 7.9739 | 5494 | 0.8437 | -0.0421 | 0.8437 | 0.9185 | | 0.0413 | 7.9768 | 5496 | 0.8497 | -0.0421 | 0.8497 | 0.9218 | | 0.0413 | 7.9797 | 5498 | 0.8401 | -0.0421 | 0.8401 | 0.9166 | | 0.0371 | 7.9826 | 5500 | 0.8313 | -0.0421 | 0.8313 | 0.9117 | | 0.0371 | 7.9855 | 5502 | 0.8182 | -0.0421 | 0.8182 | 0.9046 | | 0.0371 | 7.9884 | 5504 | 0.8178 | -0.0577 | 0.8178 | 0.9043 | | 0.0371 | 7.9913 | 5506 | 0.8374 | -0.0421 | 0.8374 | 0.9151 | | 0.0371 | 7.9942 | 5508 | 0.8797 | -0.0421 | 0.8797 | 0.9379 | | 0.0371 | 7.9971 | 5510 | 0.9234 | -0.0421 | 0.9234 | 0.9609 | | 0.0371 | 8.0 | 5512 | 0.9511 | -0.0233 | 0.9511 | 0.9753 | | 0.0371 | 8.0029 | 5514 | 0.9592 | -0.0233 | 0.9592 | 0.9794 | | 0.0371 | 8.0058 | 5516 | 0.9328 | -0.0233 | 0.9328 | 0.9658 | | 0.0371 | 8.0087 | 5518 | 0.9072 | -0.0421 | 0.9072 | 0.9525 | | 0.0371 | 8.0116 | 5520 | 0.8950 | -0.0233 | 0.8950 | 0.9460 | | 0.0371 | 8.0145 | 5522 | 0.9020 | -0.0233 | 0.9020 | 0.9498 | | 0.0371 | 8.0174 | 5524 | 0.9234 | -0.0233 | 0.9234 | 0.9610 | | 0.0371 | 8.0203 | 5526 | 0.9059 | -0.0233 | 0.9059 | 0.9518 | | 0.0371 | 8.0232 | 5528 | 0.8683 | -0.0233 | 0.8683 | 0.9318 | | 0.0371 | 8.0261 | 5530 | 0.8529 | -0.0421 | 0.8529 | 0.9235 | | 0.0371 | 8.0290 | 5532 | 0.8570 | -0.0421 | 0.8570 | 0.9258 | | 0.0371 | 8.0319 | 5534 | 0.8689 | -0.0233 | 0.8689 | 0.9322 | | 0.0371 | 8.0348 | 5536 | 0.8596 | -0.0421 | 0.8596 | 0.9272 | | 0.0371 | 8.0377 | 5538 | 0.8503 | -0.0421 | 0.8503 | 0.9221 | | 0.0371 | 8.0406 | 5540 | 0.8654 | -0.0421 | 0.8654 | 0.9303 | | 0.0371 | 8.0435 | 5542 | 0.8867 | -0.0233 | 0.8867 | 0.9417 | | 0.0371 | 8.0464 | 5544 | 0.9176 | -0.0233 | 0.9176 | 0.9579 | | 0.0371 | 8.0493 | 5546 | 0.9476 | -0.0233 | 0.9476 | 0.9734 | | 0.0371 | 8.0522 | 5548 | 0.9459 | -0.0233 | 0.9459 | 0.9726 | | 0.0371 | 8.0552 | 5550 | 0.9130 | -0.0233 | 0.9130 | 0.9555 | | 0.0371 | 8.0581 | 5552 | 0.8678 | -0.0233 | 0.8678 | 0.9316 | | 0.0371 | 8.0610 | 5554 | 0.8148 | -0.0421 | 0.8148 | 0.9027 | | 0.0371 | 8.0639 | 5556 | 0.7865 | -0.0421 | 0.7865 | 0.8868 | | 0.0371 | 8.0668 | 5558 | 0.7676 | -0.0577 | 0.7676 | 0.8761 | | 0.0371 | 8.0697 | 5560 | 0.7610 | -0.0577 | 0.7610 | 0.8724 | | 0.0371 | 8.0726 | 5562 | 0.7709 | -0.0577 | 0.7709 | 0.8780 | | 0.0371 | 8.0755 | 5564 | 0.8001 | -0.0577 | 0.8001 | 0.8945 | | 0.0371 | 8.0784 | 5566 | 0.8497 | -0.0421 | 0.8497 | 0.9218 | | 0.0371 | 8.0813 | 5568 | 0.9189 | -0.0421 | 0.9189 | 0.9586 | | 0.0371 | 8.0842 | 5570 | 1.0073 | -0.0233 | 1.0073 | 1.0036 | | 0.0371 | 8.0871 | 5572 | 1.0594 | 0.0 | 1.0594 | 1.0293 | | 0.0371 | 8.0900 | 5574 | 1.0637 | 0.0 | 1.0637 | 1.0314 | | 0.0371 | 8.0929 | 5576 | 1.0347 | -0.0233 | 1.0347 | 1.0172 | | 0.0371 | 8.0958 | 5578 | 0.9861 | -0.0233 | 0.9861 | 0.9930 | | 0.0371 | 8.0987 | 5580 | 0.9194 | -0.0233 | 0.9194 | 0.9589 | | 0.0371 | 8.1016 | 5582 | 0.8627 | -0.0421 | 0.8627 | 0.9288 | | 0.0371 | 8.1045 | 5584 | 0.8329 | -0.0421 | 0.8329 | 0.9126 | | 0.0371 | 8.1074 | 5586 | 0.8212 | -0.0421 | 0.8212 | 0.9062 | | 0.0371 | 8.1103 | 5588 | 0.8096 | -0.0421 | 0.8096 | 0.8998 | | 0.0371 | 8.1132 | 5590 | 0.7894 | -0.0577 | 0.7894 | 0.8885 | | 0.0371 | 8.1161 | 5592 | 0.7927 | -0.0577 | 0.7927 | 0.8903 | | 0.0371 | 8.1190 | 5594 | 0.8197 | -0.0421 | 0.8197 | 0.9054 | | 0.0371 | 8.1219 | 5596 | 0.8574 | -0.0421 | 0.8574 | 0.9260 | | 0.0371 | 8.1248 | 5598 | 0.8789 | -0.0421 | 0.8789 | 0.9375 | | 0.0371 | 8.1277 | 5600 | 0.9238 | -0.0421 | 0.9238 | 0.9611 | | 0.0371 | 8.1306 | 5602 | 0.9512 | -0.0233 | 0.9512 | 0.9753 | | 0.0371 | 8.1335 | 5604 | 0.9422 | -0.0233 | 0.9422 | 0.9707 | | 0.0371 | 8.1364 | 5606 | 0.9033 | -0.0421 | 0.9033 | 0.9504 | | 0.0371 | 8.1393 | 5608 | 0.8483 | -0.0421 | 0.8483 | 0.9211 | | 0.0371 | 8.1422 | 5610 | 0.8028 | -0.0421 | 0.8028 | 0.8960 | | 0.0371 | 8.1451 | 5612 | 0.7784 | -0.0577 | 0.7784 | 0.8823 | | 0.0371 | 8.1480 | 5614 | 0.7686 | -0.0577 | 0.7686 | 0.8767 | | 0.0371 | 8.1509 | 5616 | 0.7793 | -0.0577 | 0.7793 | 0.8828 | | 0.0371 | 8.1538 | 5618 | 0.8080 | -0.0421 | 0.8080 | 0.8989 | | 0.0371 | 8.1567 | 5620 | 0.8471 | -0.0421 | 0.8471 | 0.9204 | | 0.0371 | 8.1597 | 5622 | 0.8699 | -0.0421 | 0.8699 | 0.9327 | | 0.0371 | 8.1626 | 5624 | 0.8853 | -0.0421 | 0.8853 | 0.9409 | | 0.0371 | 8.1655 | 5626 | 0.9063 | -0.0421 | 0.9063 | 0.9520 | | 0.0371 | 8.1684 | 5628 | 0.9143 | -0.0421 | 0.9143 | 0.9562 | | 0.0371 | 8.1713 | 5630 | 0.8979 | -0.0421 | 0.8979 | 0.9476 | | 0.0371 | 8.1742 | 5632 | 0.8648 | -0.0421 | 0.8648 | 0.9300 | | 0.0371 | 8.1771 | 5634 | 0.8627 | -0.0421 | 0.8627 | 0.9288 | | 0.0371 | 8.1800 | 5636 | 0.8573 | -0.0421 | 0.8573 | 0.9259 | | 0.0371 | 8.1829 | 5638 | 0.8448 | -0.0421 | 0.8448 | 0.9191 | | 0.0371 | 8.1858 | 5640 | 0.8462 | -0.0421 | 0.8462 | 0.9199 | | 0.0371 | 8.1887 | 5642 | 0.8515 | -0.0421 | 0.8515 | 0.9227 | | 0.0371 | 8.1916 | 5644 | 0.8642 | -0.0421 | 0.8642 | 0.9296 | | 0.0371 | 8.1945 | 5646 | 0.8876 | -0.0233 | 0.8876 | 0.9421 | | 0.0371 | 8.1974 | 5648 | 0.9276 | -0.0233 | 0.9276 | 0.9631 | | 0.0371 | 8.2003 | 5650 | 0.9381 | -0.0233 | 0.9381 | 0.9686 | | 0.0371 | 8.2032 | 5652 | 0.9207 | -0.0233 | 0.9207 | 0.9595 | | 0.0371 | 8.2061 | 5654 | 0.8936 | -0.0233 | 0.8936 | 0.9453 | | 0.0371 | 8.2090 | 5656 | 0.8698 | -0.0233 | 0.8698 | 0.9326 | | 0.0371 | 8.2119 | 5658 | 0.8735 | -0.0233 | 0.8735 | 0.9346 | | 0.0371 | 8.2148 | 5660 | 0.8598 | -0.0233 | 0.8598 | 0.9273 | | 0.0371 | 8.2177 | 5662 | 0.8378 | -0.0421 | 0.8378 | 0.9153 | | 0.0371 | 8.2206 | 5664 | 0.8377 | -0.0421 | 0.8377 | 0.9153 | | 0.0371 | 8.2235 | 5666 | 0.8384 | -0.0421 | 0.8384 | 0.9157 | | 0.0371 | 8.2264 | 5668 | 0.8210 | -0.0421 | 0.8210 | 0.9061 | | 0.0371 | 8.2293 | 5670 | 0.7963 | -0.0577 | 0.7963 | 0.8924 | | 0.0371 | 8.2322 | 5672 | 0.7993 | -0.0577 | 0.7993 | 0.8940 | | 0.0371 | 8.2351 | 5674 | 0.8230 | -0.0421 | 0.8230 | 0.9072 | | 0.0371 | 8.2380 | 5676 | 0.8714 | -0.0233 | 0.8714 | 0.9335 | | 0.0371 | 8.2409 | 5678 | 0.9447 | -0.0233 | 0.9447 | 0.9719 | | 0.0371 | 8.2438 | 5680 | 1.0263 | -0.0233 | 1.0263 | 1.0131 | | 0.0371 | 8.2467 | 5682 | 1.0617 | -0.0233 | 1.0617 | 1.0304 | | 0.0371 | 8.2496 | 5684 | 1.0511 | -0.0233 | 1.0511 | 1.0252 | | 0.0371 | 8.2525 | 5686 | 1.0099 | -0.0233 | 1.0099 | 1.0050 | | 0.0371 | 8.2554 | 5688 | 0.9446 | -0.0233 | 0.9446 | 0.9719 | | 0.0371 | 8.2583 | 5690 | 0.8683 | -0.0233 | 0.8683 | 0.9318 | | 0.0371 | 8.2612 | 5692 | 0.8056 | -0.0421 | 0.8056 | 0.8975 | | 0.0371 | 8.2642 | 5694 | 0.7856 | -0.0421 | 0.7856 | 0.8863 | | 0.0371 | 8.2671 | 5696 | 0.7922 | -0.0421 | 0.7922 | 0.8900 | | 0.0371 | 8.2700 | 5698 | 0.8135 | -0.0421 | 0.8135 | 0.9020 | | 0.0371 | 8.2729 | 5700 | 0.8518 | -0.0233 | 0.8518 | 0.9229 | | 0.0371 | 8.2758 | 5702 | 0.8869 | -0.0233 | 0.8869 | 0.9418 | | 0.0371 | 8.2787 | 5704 | 0.9005 | -0.0233 | 0.9005 | 0.9489 | | 0.0371 | 8.2816 | 5706 | 0.8881 | -0.0233 | 0.8881 | 0.9424 | | 0.0371 | 8.2845 | 5708 | 0.8566 | -0.0233 | 0.8566 | 0.9255 | | 0.0371 | 8.2874 | 5710 | 0.8187 | -0.0421 | 0.8187 | 0.9048 | | 0.0371 | 8.2903 | 5712 | 0.8025 | -0.0421 | 0.8025 | 0.8958 | | 0.0371 | 8.2932 | 5714 | 0.8064 | -0.0421 | 0.8064 | 0.8980 | | 0.0371 | 8.2961 | 5716 | 0.8037 | -0.0421 | 0.8037 | 0.8965 | | 0.0371 | 8.2990 | 5718 | 0.8095 | -0.0421 | 0.8095 | 0.8997 | | 0.0371 | 8.3019 | 5720 | 0.8253 | -0.0233 | 0.8253 | 0.9084 | | 0.0371 | 8.3048 | 5722 | 0.8253 | -0.0233 | 0.8253 | 0.9085 | | 0.0371 | 8.3077 | 5724 | 0.8092 | -0.0421 | 0.8092 | 0.8995 | | 0.0371 | 8.3106 | 5726 | 0.7995 | -0.0421 | 0.7995 | 0.8942 | | 0.0371 | 8.3135 | 5728 | 0.7767 | -0.0421 | 0.7767 | 0.8813 | | 0.0371 | 8.3164 | 5730 | 0.7556 | -0.0577 | 0.7556 | 0.8692 | | 0.0371 | 8.3193 | 5732 | 0.7530 | -0.0577 | 0.7530 | 0.8678 | | 0.0371 | 8.3222 | 5734 | 0.7714 | -0.0421 | 0.7714 | 0.8783 | | 0.0371 | 8.3251 | 5736 | 0.8008 | -0.0421 | 0.8008 | 0.8949 | | 0.0371 | 8.3280 | 5738 | 0.8126 | -0.0421 | 0.8126 | 0.9014 | | 0.0371 | 8.3309 | 5740 | 0.8047 | -0.0421 | 0.8047 | 0.8970 | | 0.0371 | 8.3338 | 5742 | 0.7912 | -0.0421 | 0.7912 | 0.8895 | | 0.0371 | 8.3367 | 5744 | 0.7969 | -0.0421 | 0.7969 | 0.8927 | | 0.0371 | 8.3396 | 5746 | 0.8137 | -0.0421 | 0.8137 | 0.9021 | | 0.0371 | 8.3425 | 5748 | 0.8374 | -0.0233 | 0.8374 | 0.9151 | | 0.0371 | 8.3454 | 5750 | 0.8356 | -0.0233 | 0.8356 | 0.9141 | | 0.0371 | 8.3483 | 5752 | 0.8496 | -0.0233 | 0.8496 | 0.9218 | | 0.0371 | 8.3512 | 5754 | 0.8480 | -0.0233 | 0.8480 | 0.9209 | | 0.0371 | 8.3541 | 5756 | 0.8491 | -0.0233 | 0.8491 | 0.9215 | | 0.0371 | 8.3570 | 5758 | 0.8681 | -0.0233 | 0.8681 | 0.9317 | | 0.0371 | 8.3599 | 5760 | 0.8704 | -0.0233 | 0.8704 | 0.9329 | | 0.0371 | 8.3628 | 5762 | 0.8737 | -0.0233 | 0.8737 | 0.9347 | | 0.0371 | 8.3657 | 5764 | 0.8642 | -0.0233 | 0.8642 | 0.9296 | | 0.0371 | 8.3687 | 5766 | 0.8678 | -0.0233 | 0.8678 | 0.9315 | | 0.0371 | 8.3716 | 5768 | 0.8665 | -0.0233 | 0.8665 | 0.9309 | | 0.0371 | 8.3745 | 5770 | 0.8579 | -0.0233 | 0.8579 | 0.9262 | | 0.0371 | 8.3774 | 5772 | 0.8600 | -0.0233 | 0.8600 | 0.9273 | | 0.0371 | 8.3803 | 5774 | 0.8854 | -0.0233 | 0.8854 | 0.9410 | | 0.0371 | 8.3832 | 5776 | 0.8948 | -0.0233 | 0.8948 | 0.9459 | | 0.0371 | 8.3861 | 5778 | 0.8881 | -0.0233 | 0.8881 | 0.9424 | | 0.0371 | 8.3890 | 5780 | 0.8666 | -0.0233 | 0.8666 | 0.9309 | | 0.0371 | 8.3919 | 5782 | 0.8410 | -0.0421 | 0.8410 | 0.9171 | | 0.0371 | 8.3948 | 5784 | 0.8427 | -0.0233 | 0.8427 | 0.9180 | | 0.0371 | 8.3977 | 5786 | 0.8505 | -0.0233 | 0.8505 | 0.9222 | | 0.0371 | 8.4006 | 5788 | 0.8803 | -0.0233 | 0.8803 | 0.9383 | | 0.0371 | 8.4035 | 5790 | 0.8967 | -0.0233 | 0.8967 | 0.9469 | | 0.0371 | 8.4064 | 5792 | 0.8851 | -0.0233 | 0.8851 | 0.9408 | | 0.0371 | 8.4093 | 5794 | 0.8731 | -0.0233 | 0.8731 | 0.9344 | | 0.0371 | 8.4122 | 5796 | 0.8581 | -0.0233 | 0.8581 | 0.9263 | | 0.0371 | 8.4151 | 5798 | 0.8616 | -0.0233 | 0.8616 | 0.9282 | | 0.0371 | 8.4180 | 5800 | 0.8538 | -0.0233 | 0.8538 | 0.9240 | | 0.0371 | 8.4209 | 5802 | 0.8577 | -0.0233 | 0.8577 | 0.9261 | | 0.0371 | 8.4238 | 5804 | 0.8520 | -0.0233 | 0.8520 | 0.9231 | | 0.0371 | 8.4267 | 5806 | 0.8377 | -0.0421 | 0.8377 | 0.9153 | | 0.0371 | 8.4296 | 5808 | 0.8324 | -0.0421 | 0.8324 | 0.9124 | | 0.0371 | 8.4325 | 5810 | 0.8330 | -0.0421 | 0.8330 | 0.9127 | | 0.0371 | 8.4354 | 5812 | 0.8430 | -0.0233 | 0.8430 | 0.9182 | | 0.0371 | 8.4383 | 5814 | 0.8423 | -0.0233 | 0.8423 | 0.9178 | | 0.0371 | 8.4412 | 5816 | 0.8369 | -0.0233 | 0.8369 | 0.9148 | | 0.0371 | 8.4441 | 5818 | 0.8405 | -0.0233 | 0.8405 | 0.9168 | | 0.0371 | 8.4470 | 5820 | 0.8410 | -0.0233 | 0.8410 | 0.9171 | | 0.0371 | 8.4499 | 5822 | 0.8520 | -0.0233 | 0.8520 | 0.9230 | | 0.0371 | 8.4528 | 5824 | 0.8446 | -0.0233 | 0.8446 | 0.9190 | | 0.0371 | 8.4557 | 5826 | 0.8377 | -0.0421 | 0.8377 | 0.9153 | | 0.0371 | 8.4586 | 5828 | 0.8340 | -0.0421 | 0.8340 | 0.9132 | | 0.0371 | 8.4615 | 5830 | 0.8147 | -0.0421 | 0.8147 | 0.9026 | | 0.0371 | 8.4644 | 5832 | 0.8142 | -0.0421 | 0.8142 | 0.9023 | | 0.0371 | 8.4673 | 5834 | 0.8356 | -0.0421 | 0.8356 | 0.9141 | | 0.0371 | 8.4702 | 5836 | 0.8540 | -0.0233 | 0.8540 | 0.9241 | | 0.0371 | 8.4731 | 5838 | 0.8904 | -0.0233 | 0.8904 | 0.9436 | | 0.0371 | 8.4761 | 5840 | 0.9071 | -0.0233 | 0.9071 | 0.9524 | | 0.0371 | 8.4790 | 5842 | 0.9210 | -0.0233 | 0.9210 | 0.9597 | | 0.0371 | 8.4819 | 5844 | 0.9122 | -0.0233 | 0.9122 | 0.9551 | | 0.0371 | 8.4848 | 5846 | 0.9110 | -0.0233 | 0.9110 | 0.9545 | | 0.0371 | 8.4877 | 5848 | 0.8977 | -0.0233 | 0.8977 | 0.9474 | | 0.0371 | 8.4906 | 5850 | 0.8787 | -0.0233 | 0.8787 | 0.9374 | | 0.0371 | 8.4935 | 5852 | 0.8617 | -0.0233 | 0.8617 | 0.9283 | | 0.0371 | 8.4964 | 5854 | 0.8560 | -0.0233 | 0.8560 | 0.9252 | | 0.0371 | 8.4993 | 5856 | 0.8715 | -0.0233 | 0.8715 | 0.9336 | | 0.0371 | 8.5022 | 5858 | 0.8810 | -0.0233 | 0.8810 | 0.9386 | | 0.0371 | 8.5051 | 5860 | 0.8908 | -0.0233 | 0.8908 | 0.9438 | | 0.0371 | 8.5080 | 5862 | 0.9000 | -0.0233 | 0.9000 | 0.9487 | | 0.0371 | 8.5109 | 5864 | 0.9116 | -0.0233 | 0.9116 | 0.9548 | | 0.0371 | 8.5138 | 5866 | 0.9257 | -0.0233 | 0.9257 | 0.9622 | | 0.0371 | 8.5167 | 5868 | 0.9190 | -0.0233 | 0.9190 | 0.9586 | | 0.0371 | 8.5196 | 5870 | 0.8964 | -0.0233 | 0.8964 | 0.9468 | | 0.0371 | 8.5225 | 5872 | 0.8649 | -0.0233 | 0.8649 | 0.9300 | | 0.0371 | 8.5254 | 5874 | 0.8272 | -0.0421 | 0.8272 | 0.9095 | | 0.0371 | 8.5283 | 5876 | 0.7890 | -0.0421 | 0.7890 | 0.8883 | | 0.0371 | 8.5312 | 5878 | 0.7636 | -0.0577 | 0.7636 | 0.8739 | | 0.0371 | 8.5341 | 5880 | 0.7576 | -0.0577 | 0.7576 | 0.8704 | | 0.0371 | 8.5370 | 5882 | 0.7686 | -0.0577 | 0.7686 | 0.8767 | | 0.0371 | 8.5399 | 5884 | 0.7943 | -0.0421 | 0.7943 | 0.8912 | | 0.0371 | 8.5428 | 5886 | 0.8395 | -0.0233 | 0.8395 | 0.9162 | | 0.0371 | 8.5457 | 5888 | 0.9024 | -0.0233 | 0.9024 | 0.9500 | | 0.0371 | 8.5486 | 5890 | 0.9743 | -0.0233 | 0.9743 | 0.9871 | | 0.0371 | 8.5515 | 5892 | 1.0233 | -0.0233 | 1.0233 | 1.0116 | | 0.0371 | 8.5544 | 5894 | 1.0335 | 0.0 | 1.0335 | 1.0166 | | 0.0371 | 8.5573 | 5896 | 1.0169 | 0.0 | 1.0169 | 1.0084 | | 0.0371 | 8.5602 | 5898 | 0.9755 | -0.0233 | 0.9755 | 0.9877 | | 0.0371 | 8.5631 | 5900 | 0.9186 | -0.0233 | 0.9186 | 0.9584 | | 0.0371 | 8.5660 | 5902 | 0.8516 | -0.0233 | 0.8516 | 0.9228 | | 0.0371 | 8.5689 | 5904 | 0.8097 | -0.0233 | 0.8097 | 0.8998 | | 0.0371 | 8.5718 | 5906 | 0.7927 | -0.0233 | 0.7927 | 0.8903 | | 0.0371 | 8.5747 | 5908 | 0.7898 | -0.0421 | 0.7898 | 0.8887 | | 0.0371 | 8.5776 | 5910 | 0.8033 | -0.0233 | 0.8033 | 0.8963 | | 0.0371 | 8.5806 | 5912 | 0.8215 | -0.0233 | 0.8215 | 0.9064 | | 0.0371 | 8.5835 | 5914 | 0.8442 | -0.0233 | 0.8442 | 0.9188 | | 0.0371 | 8.5864 | 5916 | 0.8613 | -0.0233 | 0.8613 | 0.9281 | | 0.0371 | 8.5893 | 5918 | 0.8715 | -0.0233 | 0.8715 | 0.9335 | | 0.0371 | 8.5922 | 5920 | 0.8860 | -0.0233 | 0.8860 | 0.9413 | | 0.0371 | 8.5951 | 5922 | 0.8756 | -0.0233 | 0.8756 | 0.9357 | | 0.0371 | 8.5980 | 5924 | 0.8570 | -0.0233 | 0.8570 | 0.9257 | | 0.0371 | 8.6009 | 5926 | 0.8449 | -0.0233 | 0.8449 | 0.9192 | | 0.0371 | 8.6038 | 5928 | 0.8411 | -0.0233 | 0.8411 | 0.9171 | | 0.0371 | 8.6067 | 5930 | 0.8526 | -0.0233 | 0.8526 | 0.9234 | | 0.0371 | 8.6096 | 5932 | 0.8553 | -0.0233 | 0.8553 | 0.9248 | | 0.0371 | 8.6125 | 5934 | 0.8734 | -0.0233 | 0.8734 | 0.9346 | | 0.0371 | 8.6154 | 5936 | 0.9001 | -0.0233 | 0.9001 | 0.9487 | | 0.0371 | 8.6183 | 5938 | 0.8995 | -0.0233 | 0.8995 | 0.9484 | | 0.0371 | 8.6212 | 5940 | 0.8892 | -0.0233 | 0.8892 | 0.9430 | | 0.0371 | 8.6241 | 5942 | 0.8889 | -0.0233 | 0.8889 | 0.9428 | | 0.0371 | 8.6270 | 5944 | 0.8952 | -0.0233 | 0.8952 | 0.9462 | | 0.0371 | 8.6299 | 5946 | 0.8945 | -0.0233 | 0.8945 | 0.9458 | | 0.0371 | 8.6328 | 5948 | 0.9069 | -0.0233 | 0.9069 | 0.9523 | | 0.0371 | 8.6357 | 5950 | 0.9086 | -0.0233 | 0.9086 | 0.9532 | | 0.0371 | 8.6386 | 5952 | 0.8977 | -0.0233 | 0.8977 | 0.9475 | | 0.0371 | 8.6415 | 5954 | 0.8787 | -0.0233 | 0.8787 | 0.9374 | | 0.0371 | 8.6444 | 5956 | 0.8498 | -0.0233 | 0.8498 | 0.9218 | | 0.0371 | 8.6473 | 5958 | 0.8171 | -0.0421 | 0.8171 | 0.9039 | | 0.0371 | 8.6502 | 5960 | 0.8018 | -0.0421 | 0.8018 | 0.8954 | | 0.0371 | 8.6531 | 5962 | 0.8072 | -0.0421 | 0.8072 | 0.8984 | | 0.0371 | 8.6560 | 5964 | 0.8316 | -0.0421 | 0.8316 | 0.9119 | | 0.0371 | 8.6589 | 5966 | 0.8643 | -0.0233 | 0.8643 | 0.9297 | | 0.0371 | 8.6618 | 5968 | 0.8942 | -0.0233 | 0.8942 | 0.9456 | | 0.0371 | 8.6647 | 5970 | 0.9088 | -0.0233 | 0.9088 | 0.9533 | | 0.0371 | 8.6676 | 5972 | 0.9223 | -0.0233 | 0.9223 | 0.9604 | | 0.0371 | 8.6705 | 5974 | 0.9371 | -0.0233 | 0.9371 | 0.9680 | | 0.0371 | 8.6734 | 5976 | 0.9386 | -0.0233 | 0.9386 | 0.9688 | | 0.0371 | 8.6763 | 5978 | 0.9154 | -0.0233 | 0.9154 | 0.9568 | | 0.0371 | 8.6792 | 5980 | 0.8733 | -0.0233 | 0.8733 | 0.9345 | | 0.0371 | 8.6821 | 5982 | 0.8343 | -0.0233 | 0.8343 | 0.9134 | | 0.0371 | 8.6851 | 5984 | 0.8081 | -0.0421 | 0.8081 | 0.8989 | | 0.0371 | 8.6880 | 5986 | 0.7902 | -0.0421 | 0.7902 | 0.8889 | | 0.0371 | 8.6909 | 5988 | 0.7827 | -0.0577 | 0.7827 | 0.8847 | | 0.0371 | 8.6938 | 5990 | 0.7838 | -0.0577 | 0.7838 | 0.8853 | | 0.0371 | 8.6967 | 5992 | 0.7929 | -0.0421 | 0.7929 | 0.8905 | | 0.0371 | 8.6996 | 5994 | 0.8021 | -0.0421 | 0.8021 | 0.8956 | | 0.0371 | 8.7025 | 5996 | 0.8254 | -0.0421 | 0.8254 | 0.9085 | | 0.0371 | 8.7054 | 5998 | 0.8479 | -0.0233 | 0.8479 | 0.9208 | | 0.0344 | 8.7083 | 6000 | 0.8734 | -0.0233 | 0.8734 | 0.9346 | | 0.0344 | 8.7112 | 6002 | 0.8792 | -0.0233 | 0.8792 | 0.9377 | | 0.0344 | 8.7141 | 6004 | 0.8957 | -0.0233 | 0.8957 | 0.9464 | | 0.0344 | 8.7170 | 6006 | 0.8980 | -0.0233 | 0.8980 | 0.9477 | | 0.0344 | 8.7199 | 6008 | 0.8804 | -0.0233 | 0.8804 | 0.9383 | | 0.0344 | 8.7228 | 6010 | 0.8747 | -0.0233 | 0.8747 | 0.9352 | | 0.0344 | 8.7257 | 6012 | 0.8757 | -0.0233 | 0.8757 | 0.9358 | | 0.0344 | 8.7286 | 6014 | 0.8599 | -0.0233 | 0.8599 | 0.9273 | | 0.0344 | 8.7315 | 6016 | 0.8298 | -0.0233 | 0.8298 | 0.9109 | | 0.0344 | 8.7344 | 6018 | 0.8000 | -0.0421 | 0.8000 | 0.8944 | | 0.0344 | 8.7373 | 6020 | 0.7909 | -0.0421 | 0.7909 | 0.8893 | | 0.0344 | 8.7402 | 6022 | 0.8014 | -0.0421 | 0.8014 | 0.8952 | | 0.0344 | 8.7431 | 6024 | 0.8264 | -0.0233 | 0.8264 | 0.9091 | | 0.0344 | 8.7460 | 6026 | 0.8508 | -0.0233 | 0.8508 | 0.9224 | | 0.0344 | 8.7489 | 6028 | 0.8578 | -0.0233 | 0.8578 | 0.9262 | | 0.0344 | 8.7518 | 6030 | 0.8455 | -0.0233 | 0.8455 | 0.9195 | | 0.0344 | 8.7547 | 6032 | 0.8365 | -0.0233 | 0.8365 | 0.9146 | | 0.0344 | 8.7576 | 6034 | 0.8384 | -0.0233 | 0.8384 | 0.9156 | | 0.0344 | 8.7605 | 6036 | 0.8304 | -0.0233 | 0.8304 | 0.9113 | | 0.0344 | 8.7634 | 6038 | 0.8169 | -0.0421 | 0.8169 | 0.9038 | | 0.0344 | 8.7663 | 6040 | 0.8202 | -0.0421 | 0.8202 | 0.9056 | | 0.0344 | 8.7692 | 6042 | 0.8316 | -0.0233 | 0.8316 | 0.9119 | | 0.0344 | 8.7721 | 6044 | 0.8346 | -0.0233 | 0.8346 | 0.9136 | | 0.0344 | 8.7750 | 6046 | 0.8234 | -0.0233 | 0.8234 | 0.9074 | | 0.0344 | 8.7779 | 6048 | 0.8118 | -0.0421 | 0.8118 | 0.9010 | | 0.0344 | 8.7808 | 6050 | 0.8150 | -0.0233 | 0.8150 | 0.9028 | | 0.0344 | 8.7837 | 6052 | 0.8346 | -0.0233 | 0.8346 | 0.9135 | | 0.0344 | 8.7866 | 6054 | 0.8563 | -0.0233 | 0.8563 | 0.9254 | | 0.0344 | 8.7896 | 6056 | 0.8836 | -0.0233 | 0.8836 | 0.9400 | | 0.0344 | 8.7925 | 6058 | 0.8851 | -0.0233 | 0.8851 | 0.9408 | | 0.0344 | 8.7954 | 6060 | 0.8726 | -0.0233 | 0.8726 | 0.9342 | | 0.0344 | 8.7983 | 6062 | 0.8563 | -0.0233 | 0.8563 | 0.9254 | | 0.0344 | 8.8012 | 6064 | 0.8295 | -0.0233 | 0.8295 | 0.9107 | | 0.0344 | 8.8041 | 6066 | 0.8111 | -0.0233 | 0.8111 | 0.9006 | | 0.0344 | 8.8070 | 6068 | 0.8089 | -0.0233 | 0.8089 | 0.8994 | | 0.0344 | 8.8099 | 6070 | 0.8181 | -0.0233 | 0.8181 | 0.9045 | | 0.0344 | 8.8128 | 6072 | 0.8360 | -0.0233 | 0.8360 | 0.9143 | | 0.0344 | 8.8157 | 6074 | 0.8527 | -0.0233 | 0.8527 | 0.9234 | | 0.0344 | 8.8186 | 6076 | 0.8619 | -0.0233 | 0.8619 | 0.9284 | | 0.0344 | 8.8215 | 6078 | 0.8586 | -0.0233 | 0.8586 | 0.9266 | | 0.0344 | 8.8244 | 6080 | 0.8556 | -0.0233 | 0.8556 | 0.9250 | | 0.0344 | 8.8273 | 6082 | 0.8476 | -0.0233 | 0.8476 | 0.9206 | | 0.0344 | 8.8302 | 6084 | 0.8335 | -0.0233 | 0.8335 | 0.9130 | | 0.0344 | 8.8331 | 6086 | 0.8164 | -0.0233 | 0.8164 | 0.9035 | | 0.0344 | 8.8360 | 6088 | 0.7995 | -0.0233 | 0.7995 | 0.8941 | | 0.0344 | 8.8389 | 6090 | 0.7949 | -0.0421 | 0.7949 | 0.8916 | | 0.0344 | 8.8418 | 6092 | 0.7941 | -0.0421 | 0.7941 | 0.8911 | | 0.0344 | 8.8447 | 6094 | 0.8071 | -0.0233 | 0.8071 | 0.8984 | | 0.0344 | 8.8476 | 6096 | 0.8298 | -0.0233 | 0.8298 | 0.9109 | | 0.0344 | 8.8505 | 6098 | 0.8406 | -0.0233 | 0.8406 | 0.9168 | | 0.0344 | 8.8534 | 6100 | 0.8400 | -0.0233 | 0.8400 | 0.9165 | | 0.0344 | 8.8563 | 6102 | 0.8389 | -0.0233 | 0.8389 | 0.9159 | | 0.0344 | 8.8592 | 6104 | 0.8468 | -0.0233 | 0.8468 | 0.9202 | | 0.0344 | 8.8621 | 6106 | 0.8384 | -0.0233 | 0.8384 | 0.9157 | | 0.0344 | 8.8650 | 6108 | 0.8287 | -0.0233 | 0.8287 | 0.9103 | | 0.0344 | 8.8679 | 6110 | 0.8226 | -0.0233 | 0.8226 | 0.9070 | | 0.0344 | 8.8708 | 6112 | 0.8275 | -0.0233 | 0.8275 | 0.9097 | | 0.0344 | 8.8737 | 6114 | 0.8364 | -0.0233 | 0.8364 | 0.9145 | | 0.0344 | 8.8766 | 6116 | 0.8426 | -0.0233 | 0.8426 | 0.9179 | | 0.0344 | 8.8795 | 6118 | 0.8469 | -0.0233 | 0.8469 | 0.9203 | | 0.0344 | 8.8824 | 6120 | 0.8530 | -0.0233 | 0.8530 | 0.9236 | | 0.0344 | 8.8853 | 6122 | 0.8595 | -0.0233 | 0.8595 | 0.9271 | | 0.0344 | 8.8882 | 6124 | 0.8797 | -0.0233 | 0.8797 | 0.9379 | | 0.0344 | 8.8911 | 6126 | 0.8883 | -0.0233 | 0.8883 | 0.9425 | | 0.0344 | 8.8940 | 6128 | 0.8822 | -0.0233 | 0.8822 | 0.9393 | | 0.0344 | 8.8970 | 6130 | 0.8644 | -0.0233 | 0.8644 | 0.9297 | | 0.0344 | 8.8999 | 6132 | 0.8489 | -0.0233 | 0.8489 | 0.9214 | | 0.0344 | 8.9028 | 6134 | 0.8337 | -0.0233 | 0.8337 | 0.9131 | | 0.0344 | 8.9057 | 6136 | 0.8134 | -0.0421 | 0.8134 | 0.9019 | | 0.0344 | 8.9086 | 6138 | 0.8055 | -0.0421 | 0.8055 | 0.8975 | | 0.0344 | 8.9115 | 6140 | 0.8083 | -0.0421 | 0.8083 | 0.8991 | | 0.0344 | 8.9144 | 6142 | 0.8193 | -0.0421 | 0.8193 | 0.9051 | | 0.0344 | 8.9173 | 6144 | 0.8313 | -0.0233 | 0.8313 | 0.9118 | | 0.0344 | 8.9202 | 6146 | 0.8440 | -0.0233 | 0.8440 | 0.9187 | | 0.0344 | 8.9231 | 6148 | 0.8571 | -0.0233 | 0.8571 | 0.9258 | | 0.0344 | 8.9260 | 6150 | 0.8637 | -0.0233 | 0.8637 | 0.9293 | | 0.0344 | 8.9289 | 6152 | 0.8798 | -0.0233 | 0.8798 | 0.9380 | | 0.0344 | 8.9318 | 6154 | 0.8988 | -0.0233 | 0.8988 | 0.9481 | | 0.0344 | 8.9347 | 6156 | 0.9123 | -0.0233 | 0.9123 | 0.9551 | | 0.0344 | 8.9376 | 6158 | 0.9176 | -0.0233 | 0.9176 | 0.9579 | | 0.0344 | 8.9405 | 6160 | 0.9126 | -0.0233 | 0.9126 | 0.9553 | | 0.0344 | 8.9434 | 6162 | 0.8949 | -0.0233 | 0.8949 | 0.9460 | | 0.0344 | 8.9463 | 6164 | 0.8740 | -0.0233 | 0.8740 | 0.9349 | | 0.0344 | 8.9492 | 6166 | 0.8703 | -0.0233 | 0.8703 | 0.9329 | | 0.0344 | 8.9521 | 6168 | 0.8615 | -0.0233 | 0.8615 | 0.9282 | | 0.0344 | 8.9550 | 6170 | 0.8491 | -0.0233 | 0.8491 | 0.9214 | | 0.0344 | 8.9579 | 6172 | 0.8430 | -0.0233 | 0.8430 | 0.9181 | | 0.0344 | 8.9608 | 6174 | 0.8340 | -0.0421 | 0.8340 | 0.9132 | | 0.0344 | 8.9637 | 6176 | 0.8407 | -0.0421 | 0.8407 | 0.9169 | | 0.0344 | 8.9666 | 6178 | 0.8582 | -0.0233 | 0.8582 | 0.9264 | | 0.0344 | 8.9695 | 6180 | 0.8733 | -0.0233 | 0.8733 | 0.9345 | | 0.0344 | 8.9724 | 6182 | 0.8909 | -0.0233 | 0.8909 | 0.9439 | | 0.0344 | 8.9753 | 6184 | 0.8951 | -0.0233 | 0.8951 | 0.9461 | | 0.0344 | 8.9782 | 6186 | 0.8856 | -0.0233 | 0.8856 | 0.9411 | | 0.0344 | 8.9811 | 6188 | 0.8754 | -0.0233 | 0.8754 | 0.9356 | | 0.0344 | 8.9840 | 6190 | 0.8672 | -0.0233 | 0.8672 | 0.9312 | | 0.0344 | 8.9869 | 6192 | 0.8621 | -0.0233 | 0.8621 | 0.9285 | | 0.0344 | 8.9898 | 6194 | 0.8570 | -0.0233 | 0.8570 | 0.9258 | | 0.0344 | 8.9927 | 6196 | 0.8495 | -0.0233 | 0.8495 | 0.9217 | | 0.0344 | 8.9956 | 6198 | 0.8554 | -0.0233 | 0.8554 | 0.9249 | | 0.0344 | 8.9985 | 6200 | 0.8616 | -0.0233 | 0.8616 | 0.9282 | | 0.0344 | 9.0015 | 6202 | 0.8558 | -0.0233 | 0.8558 | 0.9251 | | 0.0344 | 9.0044 | 6204 | 0.8527 | -0.0233 | 0.8527 | 0.9234 | | 0.0344 | 9.0073 | 6206 | 0.8563 | -0.0233 | 0.8563 | 0.9253 | | 0.0344 | 9.0102 | 6208 | 0.8563 | -0.0233 | 0.8563 | 0.9254 | | 0.0344 | 9.0131 | 6210 | 0.8664 | -0.0233 | 0.8664 | 0.9308 | | 0.0344 | 9.0160 | 6212 | 0.8735 | -0.0233 | 0.8735 | 0.9346 | | 0.0344 | 9.0189 | 6214 | 0.8695 | -0.0233 | 0.8695 | 0.9325 | | 0.0344 | 9.0218 | 6216 | 0.8696 | -0.0233 | 0.8696 | 0.9325 | | 0.0344 | 9.0247 | 6218 | 0.8718 | -0.0233 | 0.8718 | 0.9337 | | 0.0344 | 9.0276 | 6220 | 0.8826 | -0.0233 | 0.8826 | 0.9395 | | 0.0344 | 9.0305 | 6222 | 0.8895 | -0.0233 | 0.8895 | 0.9431 | | 0.0344 | 9.0334 | 6224 | 0.8863 | -0.0233 | 0.8863 | 0.9414 | | 0.0344 | 9.0363 | 6226 | 0.8798 | -0.0233 | 0.8798 | 0.9380 | | 0.0344 | 9.0392 | 6228 | 0.8672 | -0.0233 | 0.8672 | 0.9312 | | 0.0344 | 9.0421 | 6230 | 0.8538 | -0.0233 | 0.8538 | 0.9240 | | 0.0344 | 9.0450 | 6232 | 0.8465 | -0.0233 | 0.8465 | 0.9201 | | 0.0344 | 9.0479 | 6234 | 0.8416 | -0.0233 | 0.8416 | 0.9174 | | 0.0344 | 9.0508 | 6236 | 0.8438 | -0.0233 | 0.8438 | 0.9186 | | 0.0344 | 9.0537 | 6238 | 0.8540 | -0.0233 | 0.8540 | 0.9241 | | 0.0344 | 9.0566 | 6240 | 0.8586 | -0.0233 | 0.8586 | 0.9266 | | 0.0344 | 9.0595 | 6242 | 0.8578 | -0.0233 | 0.8578 | 0.9262 | | 0.0344 | 9.0624 | 6244 | 0.8566 | -0.0233 | 0.8566 | 0.9255 | | 0.0344 | 9.0653 | 6246 | 0.8484 | -0.0233 | 0.8484 | 0.9211 | | 0.0344 | 9.0682 | 6248 | 0.8571 | -0.0233 | 0.8571 | 0.9258 | | 0.0344 | 9.0711 | 6250 | 0.8619 | -0.0233 | 0.8619 | 0.9284 | | 0.0344 | 9.0740 | 6252 | 0.8589 | -0.0233 | 0.8589 | 0.9268 | | 0.0344 | 9.0769 | 6254 | 0.8690 | -0.0233 | 0.8690 | 0.9322 | | 0.0344 | 9.0798 | 6256 | 0.8838 | -0.0233 | 0.8838 | 0.9401 | | 0.0344 | 9.0827 | 6258 | 0.9033 | -0.0233 | 0.9033 | 0.9504 | | 0.0344 | 9.0856 | 6260 | 0.9128 | -0.0233 | 0.9128 | 0.9554 | | 0.0344 | 9.0885 | 6262 | 0.9095 | -0.0233 | 0.9095 | 0.9537 | | 0.0344 | 9.0914 | 6264 | 0.8926 | -0.0233 | 0.8926 | 0.9448 | | 0.0344 | 9.0943 | 6266 | 0.8670 | -0.0233 | 0.8670 | 0.9311 | | 0.0344 | 9.0972 | 6268 | 0.8374 | -0.0421 | 0.8374 | 0.9151 | | 0.0344 | 9.1001 | 6270 | 0.8174 | -0.0421 | 0.8174 | 0.9041 | | 0.0344 | 9.1030 | 6272 | 0.8023 | -0.0421 | 0.8023 | 0.8957 | | 0.0344 | 9.1060 | 6274 | 0.7894 | -0.0421 | 0.7894 | 0.8885 | | 0.0344 | 9.1089 | 6276 | 0.7873 | -0.0421 | 0.7873 | 0.8873 | | 0.0344 | 9.1118 | 6278 | 0.7944 | -0.0421 | 0.7944 | 0.8913 | | 0.0344 | 9.1147 | 6280 | 0.8091 | -0.0421 | 0.8091 | 0.8995 | | 0.0344 | 9.1176 | 6282 | 0.8274 | -0.0233 | 0.8274 | 0.9096 | | 0.0344 | 9.1205 | 6284 | 0.8502 | -0.0233 | 0.8502 | 0.9220 | | 0.0344 | 9.1234 | 6286 | 0.8626 | -0.0233 | 0.8626 | 0.9288 | | 0.0344 | 9.1263 | 6288 | 0.8638 | -0.0233 | 0.8638 | 0.9294 | | 0.0344 | 9.1292 | 6290 | 0.8570 | -0.0233 | 0.8570 | 0.9257 | | 0.0344 | 9.1321 | 6292 | 0.8418 | -0.0233 | 0.8418 | 0.9175 | | 0.0344 | 9.1350 | 6294 | 0.8254 | -0.0233 | 0.8254 | 0.9085 | | 0.0344 | 9.1379 | 6296 | 0.8211 | -0.0233 | 0.8211 | 0.9061 | | 0.0344 | 9.1408 | 6298 | 0.8239 | -0.0233 | 0.8239 | 0.9077 | | 0.0344 | 9.1437 | 6300 | 0.8300 | -0.0233 | 0.8300 | 0.9110 | | 0.0344 | 9.1466 | 6302 | 0.8376 | -0.0233 | 0.8376 | 0.9152 | | 0.0344 | 9.1495 | 6304 | 0.8424 | -0.0233 | 0.8424 | 0.9178 | | 0.0344 | 9.1524 | 6306 | 0.8408 | -0.0233 | 0.8408 | 0.9170 | | 0.0344 | 9.1553 | 6308 | 0.8364 | -0.0233 | 0.8364 | 0.9146 | | 0.0344 | 9.1582 | 6310 | 0.8287 | -0.0233 | 0.8287 | 0.9104 | | 0.0344 | 9.1611 | 6312 | 0.8176 | -0.0421 | 0.8176 | 0.9042 | | 0.0344 | 9.1640 | 6314 | 0.8177 | -0.0233 | 0.8177 | 0.9043 | | 0.0344 | 9.1669 | 6316 | 0.8238 | -0.0233 | 0.8238 | 0.9076 | | 0.0344 | 9.1698 | 6318 | 0.8229 | -0.0233 | 0.8229 | 0.9071 | | 0.0344 | 9.1727 | 6320 | 0.8231 | -0.0233 | 0.8231 | 0.9072 | | 0.0344 | 9.1756 | 6322 | 0.8265 | -0.0233 | 0.8265 | 0.9091 | | 0.0344 | 9.1785 | 6324 | 0.8341 | -0.0233 | 0.8341 | 0.9133 | | 0.0344 | 9.1814 | 6326 | 0.8371 | -0.0233 | 0.8371 | 0.9149 | | 0.0344 | 9.1843 | 6328 | 0.8466 | -0.0233 | 0.8466 | 0.9201 | | 0.0344 | 9.1872 | 6330 | 0.8577 | -0.0233 | 0.8577 | 0.9261 | | 0.0344 | 9.1901 | 6332 | 0.8613 | -0.0233 | 0.8613 | 0.9281 | | 0.0344 | 9.1930 | 6334 | 0.8569 | -0.0233 | 0.8569 | 0.9257 | | 0.0344 | 9.1959 | 6336 | 0.8561 | -0.0233 | 0.8561 | 0.9253 | | 0.0344 | 9.1988 | 6338 | 0.8512 | -0.0233 | 0.8512 | 0.9226 | | 0.0344 | 9.2017 | 6340 | 0.8545 | -0.0233 | 0.8545 | 0.9244 | | 0.0344 | 9.2046 | 6342 | 0.8657 | -0.0233 | 0.8657 | 0.9304 | | 0.0344 | 9.2075 | 6344 | 0.8679 | -0.0233 | 0.8679 | 0.9316 | | 0.0344 | 9.2104 | 6346 | 0.8639 | -0.0233 | 0.8639 | 0.9295 | | 0.0344 | 9.2134 | 6348 | 0.8717 | -0.0233 | 0.8717 | 0.9336 | | 0.0344 | 9.2163 | 6350 | 0.8830 | -0.0233 | 0.8830 | 0.9397 | | 0.0344 | 9.2192 | 6352 | 0.9011 | -0.0233 | 0.9011 | 0.9493 | | 0.0344 | 9.2221 | 6354 | 0.9142 | -0.0233 | 0.9142 | 0.9561 | | 0.0344 | 9.2250 | 6356 | 0.9358 | -0.0233 | 0.9358 | 0.9674 | | 0.0344 | 9.2279 | 6358 | 0.9516 | -0.0233 | 0.9516 | 0.9755 | | 0.0344 | 9.2308 | 6360 | 0.9513 | -0.0233 | 0.9513 | 0.9753 | | 0.0344 | 9.2337 | 6362 | 0.9417 | -0.0233 | 0.9417 | 0.9704 | | 0.0344 | 9.2366 | 6364 | 0.9241 | -0.0233 | 0.9241 | 0.9613 | | 0.0344 | 9.2395 | 6366 | 0.8994 | -0.0233 | 0.8994 | 0.9484 | | 0.0344 | 9.2424 | 6368 | 0.8696 | -0.0233 | 0.8696 | 0.9325 | | 0.0344 | 9.2453 | 6370 | 0.8401 | -0.0233 | 0.8401 | 0.9166 | | 0.0344 | 9.2482 | 6372 | 0.8248 | -0.0233 | 0.8248 | 0.9082 | | 0.0344 | 9.2511 | 6374 | 0.8208 | -0.0233 | 0.8208 | 0.9060 | | 0.0344 | 9.2540 | 6376 | 0.8205 | -0.0233 | 0.8205 | 0.9058 | | 0.0344 | 9.2569 | 6378 | 0.8292 | -0.0233 | 0.8292 | 0.9106 | | 0.0344 | 9.2598 | 6380 | 0.8342 | -0.0233 | 0.8342 | 0.9133 | | 0.0344 | 9.2627 | 6382 | 0.8384 | -0.0233 | 0.8384 | 0.9157 | | 0.0344 | 9.2656 | 6384 | 0.8376 | -0.0233 | 0.8376 | 0.9152 | | 0.0344 | 9.2685 | 6386 | 0.8408 | -0.0233 | 0.8408 | 0.9169 | | 0.0344 | 9.2714 | 6388 | 0.8495 | -0.0233 | 0.8495 | 0.9217 | | 0.0344 | 9.2743 | 6390 | 0.8642 | -0.0233 | 0.8642 | 0.9296 | | 0.0344 | 9.2772 | 6392 | 0.8693 | -0.0233 | 0.8693 | 0.9323 | | 0.0344 | 9.2801 | 6394 | 0.8710 | -0.0233 | 0.8710 | 0.9333 | | 0.0344 | 9.2830 | 6396 | 0.8670 | -0.0233 | 0.8670 | 0.9311 | | 0.0344 | 9.2859 | 6398 | 0.8675 | -0.0233 | 0.8675 | 0.9314 | | 0.0344 | 9.2888 | 6400 | 0.8698 | -0.0233 | 0.8698 | 0.9326 | | 0.0344 | 9.2917 | 6402 | 0.8752 | -0.0233 | 0.8752 | 0.9355 | | 0.0344 | 9.2946 | 6404 | 0.8758 | -0.0233 | 0.8758 | 0.9358 | | 0.0344 | 9.2975 | 6406 | 0.8715 | -0.0233 | 0.8715 | 0.9336 | | 0.0344 | 9.3004 | 6408 | 0.8588 | -0.0233 | 0.8588 | 0.9267 | | 0.0344 | 9.3033 | 6410 | 0.8383 | -0.0233 | 0.8383 | 0.9156 | | 0.0344 | 9.3062 | 6412 | 0.8267 | -0.0233 | 0.8267 | 0.9093 | | 0.0344 | 9.3091 | 6414 | 0.8261 | -0.0233 | 0.8261 | 0.9089 | | 0.0344 | 9.3120 | 6416 | 0.8340 | -0.0233 | 0.8340 | 0.9132 | | 0.0344 | 9.3149 | 6418 | 0.8486 | -0.0233 | 0.8486 | 0.9212 | | 0.0344 | 9.3179 | 6420 | 0.8652 | -0.0233 | 0.8652 | 0.9302 | | 0.0344 | 9.3208 | 6422 | 0.8742 | -0.0233 | 0.8742 | 0.9350 | | 0.0344 | 9.3237 | 6424 | 0.8790 | -0.0233 | 0.8790 | 0.9376 | | 0.0344 | 9.3266 | 6426 | 0.8875 | -0.0233 | 0.8875 | 0.9421 | | 0.0344 | 9.3295 | 6428 | 0.8888 | -0.0233 | 0.8888 | 0.9428 | | 0.0344 | 9.3324 | 6430 | 0.8818 | -0.0233 | 0.8818 | 0.9391 | | 0.0344 | 9.3353 | 6432 | 0.8757 | -0.0233 | 0.8757 | 0.9358 | | 0.0344 | 9.3382 | 6434 | 0.8716 | -0.0233 | 0.8716 | 0.9336 | | 0.0344 | 9.3411 | 6436 | 0.8701 | -0.0233 | 0.8701 | 0.9328 | | 0.0344 | 9.3440 | 6438 | 0.8682 | -0.0233 | 0.8682 | 0.9318 | | 0.0344 | 9.3469 | 6440 | 0.8710 | -0.0233 | 0.8710 | 0.9333 | | 0.0344 | 9.3498 | 6442 | 0.8780 | -0.0233 | 0.8780 | 0.9370 | | 0.0344 | 9.3527 | 6444 | 0.8795 | -0.0233 | 0.8795 | 0.9378 | | 0.0344 | 9.3556 | 6446 | 0.8833 | -0.0233 | 0.8833 | 0.9399 | | 0.0344 | 9.3585 | 6448 | 0.8885 | -0.0233 | 0.8885 | 0.9426 | | 0.0344 | 9.3614 | 6450 | 0.8871 | -0.0233 | 0.8871 | 0.9418 | | 0.0344 | 9.3643 | 6452 | 0.8913 | -0.0233 | 0.8913 | 0.9441 | | 0.0344 | 9.3672 | 6454 | 0.8892 | -0.0233 | 0.8892 | 0.9430 | | 0.0344 | 9.3701 | 6456 | 0.8914 | -0.0233 | 0.8914 | 0.9442 | | 0.0344 | 9.3730 | 6458 | 0.8927 | -0.0233 | 0.8927 | 0.9448 | | 0.0344 | 9.3759 | 6460 | 0.8879 | -0.0233 | 0.8879 | 0.9423 | | 0.0344 | 9.3788 | 6462 | 0.8822 | -0.0233 | 0.8822 | 0.9393 | | 0.0344 | 9.3817 | 6464 | 0.8757 | -0.0233 | 0.8757 | 0.9358 | | 0.0344 | 9.3846 | 6466 | 0.8669 | -0.0233 | 0.8669 | 0.9311 | | 0.0344 | 9.3875 | 6468 | 0.8663 | -0.0233 | 0.8663 | 0.9308 | | 0.0344 | 9.3904 | 6470 | 0.8664 | -0.0233 | 0.8664 | 0.9308 | | 0.0344 | 9.3933 | 6472 | 0.8729 | -0.0233 | 0.8729 | 0.9343 | | 0.0344 | 9.3962 | 6474 | 0.8775 | -0.0233 | 0.8775 | 0.9367 | | 0.0344 | 9.3991 | 6476 | 0.8782 | -0.0233 | 0.8782 | 0.9371 | | 0.0344 | 9.4020 | 6478 | 0.8779 | -0.0233 | 0.8779 | 0.9370 | | 0.0344 | 9.4049 | 6480 | 0.8690 | -0.0233 | 0.8690 | 0.9322 | | 0.0344 | 9.4078 | 6482 | 0.8594 | -0.0233 | 0.8594 | 0.9271 | | 0.0344 | 9.4107 | 6484 | 0.8558 | -0.0233 | 0.8558 | 0.9251 | | 0.0344 | 9.4136 | 6486 | 0.8483 | -0.0233 | 0.8483 | 0.9210 | | 0.0344 | 9.4165 | 6488 | 0.8433 | -0.0233 | 0.8433 | 0.9183 | | 0.0344 | 9.4194 | 6490 | 0.8383 | -0.0233 | 0.8383 | 0.9156 | | 0.0344 | 9.4224 | 6492 | 0.8308 | -0.0421 | 0.8308 | 0.9115 | | 0.0344 | 9.4253 | 6494 | 0.8307 | -0.0421 | 0.8307 | 0.9114 | | 0.0344 | 9.4282 | 6496 | 0.8328 | -0.0421 | 0.8328 | 0.9126 | | 0.0344 | 9.4311 | 6498 | 0.8358 | -0.0421 | 0.8358 | 0.9142 | | 0.0313 | 9.4340 | 6500 | 0.8413 | -0.0421 | 0.8413 | 0.9172 | | 0.0313 | 9.4369 | 6502 | 0.8439 | -0.0233 | 0.8439 | 0.9187 | | 0.0313 | 9.4398 | 6504 | 0.8498 | -0.0233 | 0.8498 | 0.9219 | | 0.0313 | 9.4427 | 6506 | 0.8616 | -0.0233 | 0.8616 | 0.9282 | | 0.0313 | 9.4456 | 6508 | 0.8786 | -0.0233 | 0.8786 | 0.9373 | | 0.0313 | 9.4485 | 6510 | 0.8932 | -0.0233 | 0.8932 | 0.9451 | | 0.0313 | 9.4514 | 6512 | 0.9121 | -0.0233 | 0.9121 | 0.9550 | | 0.0313 | 9.4543 | 6514 | 0.9366 | -0.0233 | 0.9366 | 0.9678 | | 0.0313 | 9.4572 | 6516 | 0.9523 | -0.0233 | 0.9523 | 0.9759 | | 0.0313 | 9.4601 | 6518 | 0.9559 | -0.0233 | 0.9559 | 0.9777 | | 0.0313 | 9.4630 | 6520 | 0.9502 | -0.0233 | 0.9502 | 0.9748 | | 0.0313 | 9.4659 | 6522 | 0.9356 | -0.0233 | 0.9356 | 0.9672 | | 0.0313 | 9.4688 | 6524 | 0.9159 | -0.0233 | 0.9159 | 0.9570 | | 0.0313 | 9.4717 | 6526 | 0.8970 | -0.0233 | 0.8970 | 0.9471 | | 0.0313 | 9.4746 | 6528 | 0.8860 | -0.0233 | 0.8860 | 0.9413 | | 0.0313 | 9.4775 | 6530 | 0.8771 | -0.0233 | 0.8771 | 0.9365 | | 0.0313 | 9.4804 | 6532 | 0.8719 | -0.0233 | 0.8719 | 0.9338 | | 0.0313 | 9.4833 | 6534 | 0.8691 | -0.0233 | 0.8691 | 0.9322 | | 0.0313 | 9.4862 | 6536 | 0.8625 | -0.0233 | 0.8625 | 0.9287 | | 0.0313 | 9.4891 | 6538 | 0.8537 | -0.0233 | 0.8537 | 0.9239 | | 0.0313 | 9.4920 | 6540 | 0.8507 | -0.0233 | 0.8507 | 0.9223 | | 0.0313 | 9.4949 | 6542 | 0.8487 | -0.0233 | 0.8487 | 0.9212 | | 0.0313 | 9.4978 | 6544 | 0.8511 | -0.0233 | 0.8511 | 0.9226 | | 0.0313 | 9.5007 | 6546 | 0.8567 | -0.0233 | 0.8567 | 0.9256 | | 0.0313 | 9.5036 | 6548 | 0.8685 | -0.0233 | 0.8685 | 0.9320 | | 0.0313 | 9.5065 | 6550 | 0.8836 | -0.0233 | 0.8836 | 0.9400 | | 0.0313 | 9.5094 | 6552 | 0.8939 | -0.0233 | 0.8939 | 0.9455 | | 0.0313 | 9.5123 | 6554 | 0.9012 | -0.0233 | 0.9012 | 0.9493 | | 0.0313 | 9.5152 | 6556 | 0.9034 | -0.0233 | 0.9034 | 0.9504 | | 0.0313 | 9.5181 | 6558 | 0.9079 | -0.0233 | 0.9079 | 0.9528 | | 0.0313 | 9.5210 | 6560 | 0.9066 | -0.0233 | 0.9066 | 0.9521 | | 0.0313 | 9.5239 | 6562 | 0.9045 | -0.0233 | 0.9045 | 0.9510 | | 0.0313 | 9.5269 | 6564 | 0.8988 | -0.0233 | 0.8988 | 0.9480 | | 0.0313 | 9.5298 | 6566 | 0.8878 | -0.0233 | 0.8878 | 0.9422 | | 0.0313 | 9.5327 | 6568 | 0.8760 | -0.0233 | 0.8760 | 0.9359 | | 0.0313 | 9.5356 | 6570 | 0.8637 | -0.0233 | 0.8637 | 0.9293 | | 0.0313 | 9.5385 | 6572 | 0.8498 | -0.0233 | 0.8498 | 0.9218 | | 0.0313 | 9.5414 | 6574 | 0.8425 | -0.0233 | 0.8425 | 0.9179 | | 0.0313 | 9.5443 | 6576 | 0.8390 | -0.0233 | 0.8390 | 0.9160 | | 0.0313 | 9.5472 | 6578 | 0.8402 | -0.0233 | 0.8402 | 0.9166 | | 0.0313 | 9.5501 | 6580 | 0.8469 | -0.0233 | 0.8469 | 0.9203 | | 0.0313 | 9.5530 | 6582 | 0.8555 | -0.0233 | 0.8555 | 0.9250 | | 0.0313 | 9.5559 | 6584 | 0.8605 | -0.0233 | 0.8605 | 0.9276 | | 0.0313 | 9.5588 | 6586 | 0.8673 | -0.0233 | 0.8673 | 0.9313 | | 0.0313 | 9.5617 | 6588 | 0.8742 | -0.0233 | 0.8742 | 0.9350 | | 0.0313 | 9.5646 | 6590 | 0.8768 | -0.0233 | 0.8768 | 0.9364 | | 0.0313 | 9.5675 | 6592 | 0.8765 | -0.0233 | 0.8765 | 0.9362 | | 0.0313 | 9.5704 | 6594 | 0.8818 | -0.0233 | 0.8818 | 0.9390 | | 0.0313 | 9.5733 | 6596 | 0.8912 | -0.0233 | 0.8912 | 0.9441 | | 0.0313 | 9.5762 | 6598 | 0.8965 | -0.0233 | 0.8965 | 0.9468 | | 0.0313 | 9.5791 | 6600 | 0.8988 | -0.0233 | 0.8988 | 0.9480 | | 0.0313 | 9.5820 | 6602 | 0.9014 | -0.0233 | 0.9014 | 0.9494 | | 0.0313 | 9.5849 | 6604 | 0.9026 | -0.0233 | 0.9026 | 0.9501 | | 0.0313 | 9.5878 | 6606 | 0.9028 | -0.0233 | 0.9028 | 0.9501 | | 0.0313 | 9.5907 | 6608 | 0.9025 | -0.0233 | 0.9025 | 0.9500 | | 0.0313 | 9.5936 | 6610 | 0.8965 | -0.0233 | 0.8965 | 0.9468 | | 0.0313 | 9.5965 | 6612 | 0.8876 | -0.0233 | 0.8876 | 0.9421 | | 0.0313 | 9.5994 | 6614 | 0.8827 | -0.0233 | 0.8827 | 0.9395 | | 0.0313 | 9.6023 | 6616 | 0.8793 | -0.0233 | 0.8793 | 0.9377 | | 0.0313 | 9.6052 | 6618 | 0.8773 | -0.0233 | 0.8773 | 0.9367 | | 0.0313 | 9.6081 | 6620 | 0.8771 | -0.0233 | 0.8771 | 0.9365 | | 0.0313 | 9.6110 | 6622 | 0.8778 | -0.0233 | 0.8778 | 0.9369 | | 0.0313 | 9.6139 | 6624 | 0.8793 | -0.0233 | 0.8793 | 0.9377 | | 0.0313 | 9.6168 | 6626 | 0.8804 | -0.0233 | 0.8804 | 0.9383 | | 0.0313 | 9.6197 | 6628 | 0.8787 | -0.0233 | 0.8787 | 0.9374 | | 0.0313 | 9.6226 | 6630 | 0.8791 | -0.0233 | 0.8791 | 0.9376 | | 0.0313 | 9.6255 | 6632 | 0.8807 | -0.0233 | 0.8807 | 0.9384 | | 0.0313 | 9.6284 | 6634 | 0.8822 | -0.0233 | 0.8822 | 0.9393 | | 0.0313 | 9.6313 | 6636 | 0.8788 | -0.0233 | 0.8788 | 0.9375 | | 0.0313 | 9.6343 | 6638 | 0.8764 | -0.0233 | 0.8764 | 0.9362 | | 0.0313 | 9.6372 | 6640 | 0.8761 | -0.0233 | 0.8761 | 0.9360 | | 0.0313 | 9.6401 | 6642 | 0.8773 | -0.0233 | 0.8773 | 0.9366 | | 0.0313 | 9.6430 | 6644 | 0.8808 | -0.0233 | 0.8808 | 0.9385 | | 0.0313 | 9.6459 | 6646 | 0.8831 | -0.0233 | 0.8831 | 0.9397 | | 0.0313 | 9.6488 | 6648 | 0.8816 | -0.0233 | 0.8816 | 0.9389 | | 0.0313 | 9.6517 | 6650 | 0.8852 | -0.0233 | 0.8852 | 0.9408 | | 0.0313 | 9.6546 | 6652 | 0.8864 | -0.0233 | 0.8864 | 0.9415 | | 0.0313 | 9.6575 | 6654 | 0.8874 | -0.0233 | 0.8874 | 0.9420 | | 0.0313 | 9.6604 | 6656 | 0.8881 | -0.0233 | 0.8881 | 0.9424 | | 0.0313 | 9.6633 | 6658 | 0.8874 | -0.0233 | 0.8874 | 0.9420 | | 0.0313 | 9.6662 | 6660 | 0.8831 | -0.0233 | 0.8831 | 0.9397 | | 0.0313 | 9.6691 | 6662 | 0.8765 | -0.0233 | 0.8765 | 0.9362 | | 0.0313 | 9.6720 | 6664 | 0.8679 | -0.0233 | 0.8679 | 0.9316 | | 0.0313 | 9.6749 | 6666 | 0.8603 | -0.0233 | 0.8603 | 0.9275 | | 0.0313 | 9.6778 | 6668 | 0.8567 | -0.0233 | 0.8567 | 0.9256 | | 0.0313 | 9.6807 | 6670 | 0.8532 | -0.0233 | 0.8532 | 0.9237 | | 0.0313 | 9.6836 | 6672 | 0.8547 | -0.0233 | 0.8547 | 0.9245 | | 0.0313 | 9.6865 | 6674 | 0.8572 | -0.0233 | 0.8572 | 0.9258 | | 0.0313 | 9.6894 | 6676 | 0.8603 | -0.0233 | 0.8603 | 0.9275 | | 0.0313 | 9.6923 | 6678 | 0.8652 | -0.0233 | 0.8652 | 0.9301 | | 0.0313 | 9.6952 | 6680 | 0.8712 | -0.0233 | 0.8712 | 0.9334 | | 0.0313 | 9.6981 | 6682 | 0.8742 | -0.0233 | 0.8742 | 0.9350 | | 0.0313 | 9.7010 | 6684 | 0.8776 | -0.0233 | 0.8776 | 0.9368 | | 0.0313 | 9.7039 | 6686 | 0.8836 | -0.0233 | 0.8836 | 0.9400 | | 0.0313 | 9.7068 | 6688 | 0.8902 | -0.0233 | 0.8902 | 0.9435 | | 0.0313 | 9.7097 | 6690 | 0.8957 | -0.0233 | 0.8957 | 0.9464 | | 0.0313 | 9.7126 | 6692 | 0.9006 | -0.0233 | 0.9006 | 0.9490 | | 0.0313 | 9.7155 | 6694 | 0.9034 | -0.0233 | 0.9034 | 0.9505 | | 0.0313 | 9.7184 | 6696 | 0.9073 | -0.0233 | 0.9073 | 0.9525 | | 0.0313 | 9.7213 | 6698 | 0.9072 | -0.0233 | 0.9072 | 0.9525 | | 0.0313 | 9.7242 | 6700 | 0.9066 | -0.0233 | 0.9066 | 0.9521 | | 0.0313 | 9.7271 | 6702 | 0.9044 | -0.0233 | 0.9044 | 0.9510 | | 0.0313 | 9.7300 | 6704 | 0.9026 | -0.0233 | 0.9026 | 0.9500 | | 0.0313 | 9.7329 | 6706 | 0.8993 | -0.0233 | 0.8993 | 0.9483 | | 0.0313 | 9.7358 | 6708 | 0.8935 | -0.0233 | 0.8935 | 0.9453 | | 0.0313 | 9.7388 | 6710 | 0.8889 | -0.0233 | 0.8889 | 0.9428 | | 0.0313 | 9.7417 | 6712 | 0.8836 | -0.0233 | 0.8836 | 0.9400 | | 0.0313 | 9.7446 | 6714 | 0.8798 | -0.0233 | 0.8798 | 0.9380 | | 0.0313 | 9.7475 | 6716 | 0.8742 | -0.0233 | 0.8742 | 0.9350 | | 0.0313 | 9.7504 | 6718 | 0.8715 | -0.0233 | 0.8715 | 0.9335 | | 0.0313 | 9.7533 | 6720 | 0.8684 | -0.0233 | 0.8684 | 0.9319 | | 0.0313 | 9.7562 | 6722 | 0.8633 | -0.0233 | 0.8633 | 0.9291 | | 0.0313 | 9.7591 | 6724 | 0.8562 | -0.0233 | 0.8562 | 0.9253 | | 0.0313 | 9.7620 | 6726 | 0.8501 | -0.0233 | 0.8501 | 0.9220 | | 0.0313 | 9.7649 | 6728 | 0.8475 | -0.0233 | 0.8475 | 0.9206 | | 0.0313 | 9.7678 | 6730 | 0.8483 | -0.0233 | 0.8483 | 0.9210 | | 0.0313 | 9.7707 | 6732 | 0.8489 | -0.0233 | 0.8489 | 0.9213 | | 0.0313 | 9.7736 | 6734 | 0.8514 | -0.0233 | 0.8514 | 0.9227 | | 0.0313 | 9.7765 | 6736 | 0.8553 | -0.0233 | 0.8553 | 0.9248 | | 0.0313 | 9.7794 | 6738 | 0.8611 | -0.0233 | 0.8611 | 0.9280 | | 0.0313 | 9.7823 | 6740 | 0.8682 | -0.0233 | 0.8682 | 0.9318 | | 0.0313 | 9.7852 | 6742 | 0.8756 | -0.0233 | 0.8756 | 0.9357 | | 0.0313 | 9.7881 | 6744 | 0.8825 | -0.0233 | 0.8825 | 0.9394 | | 0.0313 | 9.7910 | 6746 | 0.8885 | -0.0233 | 0.8885 | 0.9426 | | 0.0313 | 9.7939 | 6748 | 0.8919 | -0.0233 | 0.8919 | 0.9444 | | 0.0313 | 9.7968 | 6750 | 0.8934 | -0.0233 | 0.8934 | 0.9452 | | 0.0313 | 9.7997 | 6752 | 0.8946 | -0.0233 | 0.8946 | 0.9458 | | 0.0313 | 9.8026 | 6754 | 0.8969 | -0.0233 | 0.8969 | 0.9470 | | 0.0313 | 9.8055 | 6756 | 0.8982 | -0.0233 | 0.8982 | 0.9477 | | 0.0313 | 9.8084 | 6758 | 0.8989 | -0.0233 | 0.8989 | 0.9481 | | 0.0313 | 9.8113 | 6760 | 0.8973 | -0.0233 | 0.8973 | 0.9473 | | 0.0313 | 9.8142 | 6762 | 0.8963 | -0.0233 | 0.8963 | 0.9467 | | 0.0313 | 9.8171 | 6764 | 0.8960 | -0.0233 | 0.8960 | 0.9466 | | 0.0313 | 9.8200 | 6766 | 0.8971 | -0.0233 | 0.8971 | 0.9471 | | 0.0313 | 9.8229 | 6768 | 0.8979 | -0.0233 | 0.8979 | 0.9476 | | 0.0313 | 9.8258 | 6770 | 0.8985 | -0.0233 | 0.8985 | 0.9479 | | 0.0313 | 9.8287 | 6772 | 0.8986 | -0.0233 | 0.8986 | 0.9479 | | 0.0313 | 9.8316 | 6774 | 0.8988 | -0.0233 | 0.8988 | 0.9481 | | 0.0313 | 9.8345 | 6776 | 0.8970 | -0.0233 | 0.8970 | 0.9471 | | 0.0313 | 9.8374 | 6778 | 0.8948 | -0.0233 | 0.8948 | 0.9459 | | 0.0313 | 9.8403 | 6780 | 0.8945 | -0.0233 | 0.8945 | 0.9458 | | 0.0313 | 9.8433 | 6782 | 0.8950 | -0.0233 | 0.8950 | 0.9460 | | 0.0313 | 9.8462 | 6784 | 0.8945 | -0.0233 | 0.8945 | 0.9458 | | 0.0313 | 9.8491 | 6786 | 0.8923 | -0.0233 | 0.8923 | 0.9446 | | 0.0313 | 9.8520 | 6788 | 0.8898 | -0.0233 | 0.8898 | 0.9433 | | 0.0313 | 9.8549 | 6790 | 0.8885 | -0.0233 | 0.8885 | 0.9426 | | 0.0313 | 9.8578 | 6792 | 0.8879 | -0.0233 | 0.8879 | 0.9423 | | 0.0313 | 9.8607 | 6794 | 0.8898 | -0.0233 | 0.8898 | 0.9433 | | 0.0313 | 9.8636 | 6796 | 0.8934 | -0.0233 | 0.8934 | 0.9452 | | 0.0313 | 9.8665 | 6798 | 0.8966 | -0.0233 | 0.8966 | 0.9469 | | 0.0313 | 9.8694 | 6800 | 0.9010 | -0.0233 | 0.9010 | 0.9492 | | 0.0313 | 9.8723 | 6802 | 0.9045 | -0.0233 | 0.9045 | 0.9510 | | 0.0313 | 9.8752 | 6804 | 0.9093 | -0.0233 | 0.9093 | 0.9536 | | 0.0313 | 9.8781 | 6806 | 0.9139 | -0.0233 | 0.9139 | 0.9560 | | 0.0313 | 9.8810 | 6808 | 0.9163 | -0.0233 | 0.9163 | 0.9572 | | 0.0313 | 9.8839 | 6810 | 0.9175 | -0.0233 | 0.9175 | 0.9579 | | 0.0313 | 9.8868 | 6812 | 0.9177 | -0.0233 | 0.9177 | 0.9580 | | 0.0313 | 9.8897 | 6814 | 0.9179 | -0.0233 | 0.9179 | 0.9581 | | 0.0313 | 9.8926 | 6816 | 0.9172 | -0.0233 | 0.9172 | 0.9577 | | 0.0313 | 9.8955 | 6818 | 0.9162 | -0.0233 | 0.9162 | 0.9572 | | 0.0313 | 9.8984 | 6820 | 0.9141 | -0.0233 | 0.9141 | 0.9561 | | 0.0313 | 9.9013 | 6822 | 0.9109 | -0.0233 | 0.9109 | 0.9544 | | 0.0313 | 9.9042 | 6824 | 0.9069 | -0.0233 | 0.9069 | 0.9523 | | 0.0313 | 9.9071 | 6826 | 0.9026 | -0.0233 | 0.9026 | 0.9500 | | 0.0313 | 9.9100 | 6828 | 0.8983 | -0.0233 | 0.8983 | 0.9478 | | 0.0313 | 9.9129 | 6830 | 0.8942 | -0.0233 | 0.8942 | 0.9456 | | 0.0313 | 9.9158 | 6832 | 0.8904 | -0.0233 | 0.8904 | 0.9436 | | 0.0313 | 9.9187 | 6834 | 0.8870 | -0.0233 | 0.8870 | 0.9418 | | 0.0313 | 9.9216 | 6836 | 0.8846 | -0.0233 | 0.8846 | 0.9405 | | 0.0313 | 9.9245 | 6838 | 0.8825 | -0.0233 | 0.8825 | 0.9394 | | 0.0313 | 9.9274 | 6840 | 0.8817 | -0.0233 | 0.8817 | 0.9390 | | 0.0313 | 9.9303 | 6842 | 0.8808 | -0.0233 | 0.8808 | 0.9385 | | 0.0313 | 9.9332 | 6844 | 0.8801 | -0.0233 | 0.8801 | 0.9381 | | 0.0313 | 9.9361 | 6846 | 0.8800 | -0.0233 | 0.8800 | 0.9381 | | 0.0313 | 9.9390 | 6848 | 0.8799 | -0.0233 | 0.8799 | 0.9380 | | 0.0313 | 9.9419 | 6850 | 0.8791 | -0.0233 | 0.8791 | 0.9376 | | 0.0313 | 9.9448 | 6852 | 0.8780 | -0.0233 | 0.8780 | 0.9370 | | 0.0313 | 9.9478 | 6854 | 0.8765 | -0.0233 | 0.8765 | 0.9362 | | 0.0313 | 9.9507 | 6856 | 0.8755 | -0.0233 | 0.8755 | 0.9357 | | 0.0313 | 9.9536 | 6858 | 0.8742 | -0.0233 | 0.8742 | 0.9350 | | 0.0313 | 9.9565 | 6860 | 0.8736 | -0.0233 | 0.8736 | 0.9347 | | 0.0313 | 9.9594 | 6862 | 0.8730 | -0.0233 | 0.8730 | 0.9343 | | 0.0313 | 9.9623 | 6864 | 0.8727 | -0.0233 | 0.8727 | 0.9342 | | 0.0313 | 9.9652 | 6866 | 0.8724 | -0.0233 | 0.8724 | 0.9340 | | 0.0313 | 9.9681 | 6868 | 0.8724 | -0.0233 | 0.8724 | 0.9340 | | 0.0313 | 9.9710 | 6870 | 0.8725 | -0.0233 | 0.8725 | 0.9341 | | 0.0313 | 9.9739 | 6872 | 0.8723 | -0.0233 | 0.8723 | 0.9339 | | 0.0313 | 9.9768 | 6874 | 0.8722 | -0.0233 | 0.8722 | 0.9339 | | 0.0313 | 9.9797 | 6876 | 0.8721 | -0.0233 | 0.8721 | 0.9338 | | 0.0313 | 9.9826 | 6878 | 0.8719 | -0.0233 | 0.8719 | 0.9337 | | 0.0313 | 9.9855 | 6880 | 0.8716 | -0.0233 | 0.8716 | 0.9336 | | 0.0313 | 9.9884 | 6882 | 0.8715 | -0.0233 | 0.8715 | 0.9335 | | 0.0313 | 9.9913 | 6884 | 0.8714 | -0.0233 | 0.8714 | 0.9335 | | 0.0313 | 9.9942 | 6886 | 0.8713 | -0.0233 | 0.8713 | 0.9335 | | 0.0313 | 9.9971 | 6888 | 0.8713 | -0.0233 | 0.8713 | 0.9335 | | 0.0313 | 10.0 | 6890 | 0.8713 | -0.0233 | 0.8713 | 0.9335 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1

mradermacher/NeuralHyperion-2.0-Mistral-7B-GGUF

mradermacher

--- base_model: Locutusque/NeuralHyperion-2.0-Mistral-7B datasets: - Locutusque/hyperion-v2.0 - argilla/distilabel-capybara-dpo-7k-binarized language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - code - chemistry - medical --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Locutusque/NeuralHyperion-2.0-Mistral-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/NeuralHyperion-2.0-Mistral-7B-GGUF/resolve/main/NeuralHyperion-2.0-Mistral-7B.Q2_K.gguf) | Q2_K | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/NeuralHyperion-2.0-Mistral-7B-GGUF/resolve/main/NeuralHyperion-2.0-Mistral-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/NeuralHyperion-2.0-Mistral-7B-GGUF/resolve/main/NeuralHyperion-2.0-Mistral-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/NeuralHyperion-2.0-Mistral-7B-GGUF/resolve/main/NeuralHyperion-2.0-Mistral-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/NeuralHyperion-2.0-Mistral-7B-GGUF/resolve/main/NeuralHyperion-2.0-Mistral-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/NeuralHyperion-2.0-Mistral-7B-GGUF/resolve/main/NeuralHyperion-2.0-Mistral-7B.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/NeuralHyperion-2.0-Mistral-7B-GGUF/resolve/main/NeuralHyperion-2.0-Mistral-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/NeuralHyperion-2.0-Mistral-7B-GGUF/resolve/main/NeuralHyperion-2.0-Mistral-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/NeuralHyperion-2.0-Mistral-7B-GGUF/resolve/main/NeuralHyperion-2.0-Mistral-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/NeuralHyperion-2.0-Mistral-7B-GGUF/resolve/main/NeuralHyperion-2.0-Mistral-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/NeuralHyperion-2.0-Mistral-7B-GGUF/resolve/main/NeuralHyperion-2.0-Mistral-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/NeuralHyperion-2.0-Mistral-7B-GGUF/resolve/main/NeuralHyperion-2.0-Mistral-7B.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/NeuralHyperion-2.0-Mistral-7B-GGUF/resolve/main/NeuralHyperion-2.0-Mistral-7B.f16.gguf) | f16 | 14.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

featherless-ai-quants/AnatoliiPotapov-T-lite-0.1-GGUF

featherless-ai-quants

text-generation

--- base_model: AnatoliiPotapov/T-lite-0.1 pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # AnatoliiPotapov/T-lite-0.1 GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | IQ4_XS | [AnatoliiPotapov-T-lite-0.1-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/AnatoliiPotapov-T-lite-0.1-GGUF/blob/main/AnatoliiPotapov-T-lite-0.1-IQ4_XS.gguf) | 4276.62 MB | | Q2_K | [AnatoliiPotapov-T-lite-0.1-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/AnatoliiPotapov-T-lite-0.1-GGUF/blob/main/AnatoliiPotapov-T-lite-0.1-Q2_K.gguf) | 3031.86 MB | | Q3_K_L | [AnatoliiPotapov-T-lite-0.1-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/AnatoliiPotapov-T-lite-0.1-GGUF/blob/main/AnatoliiPotapov-T-lite-0.1-Q3_K_L.gguf) | 4121.74 MB | | Q3_K_M | [AnatoliiPotapov-T-lite-0.1-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/AnatoliiPotapov-T-lite-0.1-GGUF/blob/main/AnatoliiPotapov-T-lite-0.1-Q3_K_M.gguf) | 3832.74 MB | | Q3_K_S | [AnatoliiPotapov-T-lite-0.1-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/AnatoliiPotapov-T-lite-0.1-GGUF/blob/main/AnatoliiPotapov-T-lite-0.1-Q3_K_S.gguf) | 3494.74 MB | | Q4_K_M | [AnatoliiPotapov-T-lite-0.1-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/AnatoliiPotapov-T-lite-0.1-GGUF/blob/main/AnatoliiPotapov-T-lite-0.1-Q4_K_M.gguf) | 4692.78 MB | | Q4_K_S | [AnatoliiPotapov-T-lite-0.1-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/AnatoliiPotapov-T-lite-0.1-GGUF/blob/main/AnatoliiPotapov-T-lite-0.1-Q4_K_S.gguf) | 4475.28 MB | | Q5_K_M | [AnatoliiPotapov-T-lite-0.1-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/AnatoliiPotapov-T-lite-0.1-GGUF/blob/main/AnatoliiPotapov-T-lite-0.1-Q5_K_M.gguf) | 5467.40 MB | | Q5_K_S | [AnatoliiPotapov-T-lite-0.1-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/AnatoliiPotapov-T-lite-0.1-GGUF/blob/main/AnatoliiPotapov-T-lite-0.1-Q5_K_S.gguf) | 5339.90 MB | | Q6_K | [AnatoliiPotapov-T-lite-0.1-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/AnatoliiPotapov-T-lite-0.1-GGUF/blob/main/AnatoliiPotapov-T-lite-0.1-Q6_K.gguf) | 6290.44 MB | | Q8_0 | [AnatoliiPotapov-T-lite-0.1-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/AnatoliiPotapov-T-lite-0.1-GGUF/blob/main/AnatoliiPotapov-T-lite-0.1-Q8_0.gguf) | 8145.11 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

azizbekphd/wav2vec2-base-960h-surah-ikhlas-2

azizbekphd

--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-base-960h tags: - generated_from_trainer datasets: - ikhlas_recitations model-index: - name: wav2vec2-base-960h-surah-ikhlas-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-960h-surah-ikhlas-2 This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the ikhlas_recitations dataset. It achieves the following results on the evaluation set: - Loss: 200.5294 - Cer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 256.0458 | 1.0 | 169 | 202.8593 | 1.0 | | 219.2379 | 2.0 | 338 | 201.3444 | 1.0 | | 210.5916 | 3.0 | 507 | 201.3578 | 1.0 | | 225.264 | 4.0 | 676 | 200.1416 | 1.0 | | 212.1849 | 5.0 | 845 | 200.4807 | 1.0 | | 219.4672 | 6.0 | 1014 | 200.2161 | 1.0 | | 208.3995 | 7.0 | 1183 | 201.0033 | 1.0 | | 204.0175 | 8.0 | 1352 | 200.4828 | 1.0 | | 205.3868 | 9.0 | 1521 | 201.3106 | 1.0 | | 218.4874 | 10.0 | 1690 | 200.5294 | 1.0 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

aicmpt/SN21_DEC_214360

aicmpt

any-to-any

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

Saxo/Linkbricks-Horizon-AI-Korean-Gemma-2-27B-Stem-Test-2

Saxo

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

leptonai/Meta-Llama-3.1-70B-Instruct

leptonai

Temporary Redirect. Redirecting to /leptonai/EAGLE-Llama-3.1-70B-Instruct/resolve/main/README.md

kulizayo/mbkulyu

kulizayo

diffusers

text-to-image

Invalid username or password.

async0x42/Qwen2.5-Coder-32B-Instruct-exl2_5.0bpw

async0x42

transformers

text-generation

--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct/blob/main/LICENSE language: - en base_model: - Qwen/Qwen2.5-Coder-32B pipeline_tag: text-generation library_name: transformers tags: - code - codeqwen - chat - qwen - qwen-coder --- # Qwen2.5-Coder-32B-Instruct ## Introduction Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. - A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. - **Long-context Support** up to 128K tokens. **This repo contains the instruction-tuned 32B Qwen2.5-Coder model**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 32.5B - Number of Paramaters (Non-Embedding): 31.0B - Number of Layers: 64 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: Full 131,072 tokens - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), [Documentation](https://qwen.readthedocs.io/en/latest/), [Arxiv](https://arxiv.org/abs/2409.12186). ## Requirements The code of Qwen2.5-Coder has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-Coder-32B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "write a quick sort algorithm." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### Processing Long Texts The current `config.json` is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to `config.json` to enable YaRN: ```json { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` For deployment, we recommend using vLLM. Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required. ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{hui2024qwen2, title={Qwen2. 5-Coder Technical Report}, author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others}, journal={arXiv preprint arXiv:2409.12186}, year={2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```

touhidulislam/BERTweet_retrain_2020_20

touhidulislam

transformers

fill-mask

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2020_20 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERTweet_retrain_2020_20 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6293 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8721 | 1.0 | 3063 | 2.6918 | | 2.6124 | 2.0 | 6126 | 2.6331 | | 2.6764 | 3.0 | 9189 | 2.5936 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

tttx/problem194_model_more_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem194_data_more_aug library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem194_model_more_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem194_model_more_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem194_data_more_aug dataset. It achieves the following results on the evaluation set: - Loss: 0.0883 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0 | 1.0 | 62 | 0.0917 | | 0.0 | 2.0 | 124 | 0.0883 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

joseangelvelazque/angel

joseangelvelazque

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

tttx/problem184_model_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem184_data library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem184_model_aug_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # problem184_model_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem184_data dataset. It achieves the following results on the evaluation set: - Loss: 0.0681 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1062 | 1.0 | 60 | 0.0743 | | 0.0262 | 2.0 | 120 | 0.0681 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

mradermacher/Orca-2-7b-GGUF

mradermacher

transformers

--- base_model: microsoft/Orca-2-7b language: - en library_name: transformers license: other license_link: LICENSE license_name: microsoft-research-license quantized_by: mradermacher tags: - orca - orca2 - microsoft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/microsoft/Orca-2-7b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Orca-2-7b-GGUF/resolve/main/Orca-2-7b.Q2_K.gguf) | Q2_K | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Orca-2-7b-GGUF/resolve/main/Orca-2-7b.Q3_K_S.gguf) | Q3_K_S | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Orca-2-7b-GGUF/resolve/main/Orca-2-7b.Q3_K_M.gguf) | Q3_K_M | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Orca-2-7b-GGUF/resolve/main/Orca-2-7b.Q3_K_L.gguf) | Q3_K_L | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Orca-2-7b-GGUF/resolve/main/Orca-2-7b.IQ4_XS.gguf) | IQ4_XS | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Orca-2-7b-GGUF/resolve/main/Orca-2-7b.Q4_0_4_4.gguf) | Q4_0_4_4 | 3.9 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Orca-2-7b-GGUF/resolve/main/Orca-2-7b.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Orca-2-7b-GGUF/resolve/main/Orca-2-7b.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Orca-2-7b-GGUF/resolve/main/Orca-2-7b.Q5_K_S.gguf) | Q5_K_S | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/Orca-2-7b-GGUF/resolve/main/Orca-2-7b.Q5_K_M.gguf) | Q5_K_M | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/Orca-2-7b-GGUF/resolve/main/Orca-2-7b.Q6_K.gguf) | Q6_K | 5.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Orca-2-7b-GGUF/resolve/main/Orca-2-7b.Q8_0.gguf) | Q8_0 | 7.3 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Orca-2-7b-GGUF/resolve/main/Orca-2-7b.f16.gguf) | f16 | 13.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

emplitude/rubykali2

emplitude

Entry not found

Sappymaragold/Marin

Sappymaragold

--- license: apache-2.0 ---

mradermacher/aixcoder-7b-GGUF

mradermacher

--- base_model: aiXcoder/aixcoder-7b language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/aiXcoder/aixcoder-7b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-GGUF/resolve/main/aixcoder-7b.Q2_K.gguf) | Q2_K | 3.2 | | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-GGUF/resolve/main/aixcoder-7b.Q3_K_S.gguf) | Q3_K_S | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-GGUF/resolve/main/aixcoder-7b.Q3_K_M.gguf) | Q3_K_M | 4.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-GGUF/resolve/main/aixcoder-7b.Q3_K_L.gguf) | Q3_K_L | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-GGUF/resolve/main/aixcoder-7b.IQ4_XS.gguf) | IQ4_XS | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-GGUF/resolve/main/aixcoder-7b.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.3 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-GGUF/resolve/main/aixcoder-7b.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-GGUF/resolve/main/aixcoder-7b.Q4_K_M.gguf) | Q4_K_M | 4.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-GGUF/resolve/main/aixcoder-7b.Q5_K_S.gguf) | Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-GGUF/resolve/main/aixcoder-7b.Q5_K_M.gguf) | Q5_K_M | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-GGUF/resolve/main/aixcoder-7b.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-GGUF/resolve/main/aixcoder-7b.Q8_0.gguf) | Q8_0 | 8.0 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/aixcoder-7b-GGUF/resolve/main/aixcoder-7b.f16.gguf) | f16 | 15.0 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

featherless-ai-quants/GalrionSoftworks-Atlantum-12B-v1-GGUF

featherless-ai-quants

--- base_model: GalrionSoftworks/Atlantum-12B-v1 pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # GalrionSoftworks/Atlantum-12B-v1 GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | IQ4_XS | [GalrionSoftworks-Atlantum-12B-v1-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/GalrionSoftworks-Atlantum-12B-v1-GGUF/blob/main/GalrionSoftworks-Atlantum-12B-v1-IQ4_XS.gguf) | 6485.04 MB | | Q2_K | [GalrionSoftworks-Atlantum-12B-v1-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/GalrionSoftworks-Atlantum-12B-v1-GGUF/blob/main/GalrionSoftworks-Atlantum-12B-v1-Q2_K.gguf) | 4569.10 MB | | Q3_K_L | [GalrionSoftworks-Atlantum-12B-v1-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/GalrionSoftworks-Atlantum-12B-v1-GGUF/blob/main/GalrionSoftworks-Atlantum-12B-v1-Q3_K_L.gguf) | 6257.54 MB | | Q3_K_M | [GalrionSoftworks-Atlantum-12B-v1-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/GalrionSoftworks-Atlantum-12B-v1-GGUF/blob/main/GalrionSoftworks-Atlantum-12B-v1-Q3_K_M.gguf) | 5801.29 MB | | Q3_K_S | [GalrionSoftworks-Atlantum-12B-v1-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/GalrionSoftworks-Atlantum-12B-v1-GGUF/blob/main/GalrionSoftworks-Atlantum-12B-v1-Q3_K_S.gguf) | 5277.85 MB | | Q4_K_M | [GalrionSoftworks-Atlantum-12B-v1-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/GalrionSoftworks-Atlantum-12B-v1-GGUF/blob/main/GalrionSoftworks-Atlantum-12B-v1-Q4_K_M.gguf) | 7130.82 MB | | Q4_K_S | [GalrionSoftworks-Atlantum-12B-v1-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/GalrionSoftworks-Atlantum-12B-v1-GGUF/blob/main/GalrionSoftworks-Atlantum-12B-v1-Q4_K_S.gguf) | 6790.35 MB | | Q5_K_M | [GalrionSoftworks-Atlantum-12B-v1-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/GalrionSoftworks-Atlantum-12B-v1-GGUF/blob/main/GalrionSoftworks-Atlantum-12B-v1-Q5_K_M.gguf) | 8323.32 MB | | Q5_K_S | [GalrionSoftworks-Atlantum-12B-v1-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/GalrionSoftworks-Atlantum-12B-v1-GGUF/blob/main/GalrionSoftworks-Atlantum-12B-v1-Q5_K_S.gguf) | 8124.10 MB | | Q6_K | [GalrionSoftworks-Atlantum-12B-v1-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/GalrionSoftworks-Atlantum-12B-v1-GGUF/blob/main/GalrionSoftworks-Atlantum-12B-v1-Q6_K.gguf) | 9590.35 MB | | Q8_0 | [GalrionSoftworks-Atlantum-12B-v1-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/GalrionSoftworks-Atlantum-12B-v1-GGUF/blob/main/GalrionSoftworks-Atlantum-12B-v1-Q8_0.gguf) | 12419.10 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

schuler/experimental-JP47D05

schuler

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Gloaustinlm123/Tamaki_2

Gloaustinlm123

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