Wonder-Griffin
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library_name: transformers
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license: unlicense
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language:
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- en
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metrics:
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- accuracy
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---
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- 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. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[
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---
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license: unlicense
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datasets:
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- proj-persona/PersonaHub
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- Salesforce/xlam-function-calling-60k
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- BAAI/Infinity-Instruct
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- HuggingFaceFW/fineweb
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- HuggingFaceTB/smollm-corpus
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- wikimedia/wikipedia
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- Open-Orca/OpenOrca
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language:
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- en
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metrics:
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- accuracy
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- precision
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- bleu
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- recall
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- hack/test_metric
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library_name: -transformers
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pipeline_tag: -text-generation
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tags:
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- text-generation-inference
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- question answering
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- logic
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- reasoning
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- text-classification
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---
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Model Card for Model ID
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LLM for Text Generation
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Model Details
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JudgeXL-LLM is a large language model designed for text generation, classification and question answering using logic and reasoning to predict the next sequence of tokens
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Model Description
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Model is designed to have no restrictions or barriers, use it at your own risk.
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Developed by: WonGrifferousAI
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Funded by [optional]: WonGrifferousAI
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Shared by [optional]: Judge_Mrogan
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Model type: Transformer LLM
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Language(s) (NLP): English
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License: Unlicense
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Uses
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Use for anything you can train it to do.
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Out-of-Scope Use
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This model is good for everything
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Bias, Risks, and Limitations
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No limitations
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Recommendations
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Recommend everyone should use as needed.
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---
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tags:
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- text-generation-inference
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- not-for-all-audiences
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base_model: TransformersStyleXL
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instance_prompt: null
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license: unlicense
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datasets:
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- proj-persona/PersonaHub
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- Salesforce/xlam-function-calling-60k
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- BAAI/Infinity-Instruct
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- HuggingFaceFW/fineweb
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- wikimedia/wikipedia
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language:
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- en
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metrics:
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- accuracy
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- code_eval
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library_name: transformers
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pipeline_tag: text-generation
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---
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# JudgeLLM
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## Download model
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Weights for this model are available in PyTorch,Safetensors format.
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[Download](/Wonder-Griffin/JudgeLLM/tree/main) them in the Files & versions tab.
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