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  library_name: transformers
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  license: apache-2.0
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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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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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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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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- ### Results
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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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- ### Compute Infrastructure
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- #### Hardware
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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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- **APA:**
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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 [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  ---
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  library_name: transformers
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  license: apache-2.0
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+ pipeline_tag: text-generation
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+ datasets:
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+ - maywell/ko_Ultrafeedback_binarized
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+ base model:
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+ - yanolja/EEVE-Korean-Instruct-10.8B-v1.0
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  ---
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f22e4076fedc4fd11e978f/MoTedec_ZL8GM2MmGyAPs.png)
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+ # T3Q-LLM-MG-v1.0
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+ ## This model is a version of T3Q-LLM/T3Q-LLM-solar10.8-sft-v1.0 that has been fine-tuned with DPO.
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+ ## Model Developers Chihoon Lee(chihoonlee10), T3Q
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+ ### Python code
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+ ```python
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+ import torch
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+ from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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+ MODEL_DIR = "chihoonlee10/T3Q-LLM-MG-v1.0"
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+ model = AutoModelForCausalLM.from_pretrained(MODEL_DIR, torch_dtype=torch.float16).to("cuda")
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+ tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
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+ streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
 
 
 
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+ s = "한국의 수도는 어디?"
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+ conversation = [{'role': 'user', 'content': s}]
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+ inputs = tokenizer.apply_chat_template(
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+ conversation,
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+ tokenize=True,
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+ add_generation_prompt=True,
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+ return_tensors='pt').to("cuda")
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+ _ = model.generate(inputs, streamer=streamer, max_new_tokens=1024)
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+ ```
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+ hf (pretrained=chihoonlee10/T3Q-LLM-MG-v1.0), limit: None, provide_description: False, num_fewshot: 0, batch_size: None
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+ | Task |Version| Metric |Value | |Stderr|
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+ |----------------|------:|--------|-----:|---|-----:|
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+ |kobest_boolq | 0|acc |0.9523|± |0.0057|
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+ | | |macro_f1|0.9523|± |0.0057|
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+ |kobest_copa | 0|acc |0.7740|± |0.0132|
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+ | | |macro_f1|0.7737|± |0.0133|
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+ |kobest_hellaswag| 0|acc |0.4980|± |0.0224|
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+ | | |acc_norm|0.5920|± |0.0220|
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+ | | |macro_f1|0.4950|± |0.0223|
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+ |kobest_sentineg | 0|acc |0.7254|± |0.0224|
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+ | | |macro_f1|0.7106|± |0.0234|
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+ ### T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0
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+ | Task |Version| Metric |Value | |Stderr|
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+ |----------------|------:|--------|-----:|---|-----:|
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+ |kobest_boolq | 0|acc |0.9387|± |0.0064|
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+ | | |macro_f1|0.9387|± |0.0064|
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+ |kobest_copa | 0|acc |0.7590|± |0.0135|
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+ | | |macro_f1|0.7585|± |0.0135|
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+ |kobest_hellaswag| 0|acc |0.5080|± |0.0224|
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+ | | |acc_norm|0.5580|± |0.0222|
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+ | | |macro_f1|0.5049|± |0.0224|
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+ |kobest_sentineg | 0|acc |0.8489|± |0.0180|
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+ | | |macro_f1|0.8483|± |0.0180|