Add model card numbers to Dracarys2-Llama3.1-70B-Instruct model
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            library_name: transformers
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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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            ## Uses
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            ### Direct Use
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            [More Information Needed]
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            ## Bias, Risks, and Limitations
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            [More Information Needed]
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            ### Recommendations
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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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            [More Information Needed]
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            ### Training Procedure
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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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            [More Information Needed]
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            ## Evaluation
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            ### Testing Data, Factors & Metrics
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            #### Testing Data
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            [More Information Needed]
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            #### Factors
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            [More Information Needed]
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            #### Metrics
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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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            [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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            license: llama3
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            library_name: transformers
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            # Dracarys2-Llama-3.1-70B-Instruct
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            ### Built with Meta Llama 3
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            # Introduction
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            We introduce the latest in the Smaug series, the Dracarys family of finetunes targeting coding performance improvements
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            across a variety of base models.
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            This variant is a finetune of [meta-llama/Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct)
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            Compared to meta-llama/Meta-Llama-3.1-70B-Instruct, Dracarys has better LiveCodeBench scores (see evaluation results below).
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            ### Model Description
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            - **Developed by:** [Abacus.AI](https://abacus.ai)
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            - **License:** https://llama.meta.com/llama3/license/
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            - **Finetuned from model:** [meta-llama/Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct).
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            ## How to use
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            The prompt format is unchanged from Llama 3 70B Instruct (see evaluations for prompt details for LCB)
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            ### Use with transformers
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            See the snippet below for usage with Transformers:
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            ```python
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            import transformers
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            import torch
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            model_id = "abacusai/Dracarys-72B-Instruct"
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            pipeline = transformers.pipeline(
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                "text-generation",
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                model=model_id,
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                model_kwargs={"torch_dtype": torch.bfloat16},
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                device_map="auto",
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            )
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            messages = [
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                {"role": "system", "content": "You are data science coding assistant that generates Python code using Pandas and Numpy."},
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                {"role": "user", "content": "Write code to select rows from the dataframe `df` having the maximum `temp` for each `city`"},
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            ]
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            prompt = pipeline.tokenizer.apply_chat_template(
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            		messages, 
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            		tokenize=False, 
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            		add_generation_prompt=True
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            )
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            terminators = [
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                pipeline.tokenizer.eos_token_id,
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                pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>"),
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                pipeline.tokenizer.convert_tokens_to_ids("<|end_of_text|>"),
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            ]
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            outputs = pipeline(
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                prompt,
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                max_new_tokens=256,
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                eos_token_id=terminators,
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                do_sample=True,
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                temperature=0.6,
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                top_p=0.9,
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            )
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            print(outputs[0]["generated_text"][len(prompt):])
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            ```
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            # Evaluation Results
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            ## LiveCodeBench
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            | Model                               | Code Generation | Code Execution |Test Output Prediction |
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            |-------------------------------------|-----------------|----------------|-----------------------|
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            | **Dracarys2-Llama-3.1-70B-Instruct**| **33.44**       | 48.26          | **52.10**             |
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            | Meta-Llama-3.1-70B-Instruct         | 32.23           | 48.768         | 41.40                 |
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            ## Breakdown of LiveCodeBench CodeGeneration
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            | Model                               | Easy            | Medium         | Hard                  |
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            |-------------------------------------|-----------------|----------------|-----------------------|
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            | **Dracarys2-Llama-3.1-70B-Instruct**| **71.29**       | **18.48**      | **3.57**              |
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            | Meta-Llama-3.1-70B-Instruct         | 68.4            | 17.99          | 3.57                  |
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            ## Breakdown of LiveCodeBench CodeExecution
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            | Model                               | COT             | Non-COT        |
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            |-------------------------------------|-----------------|----------------|
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            | **Dracarys2-Llama-3.1-70B-Instruct**| **75.55**       | 48.26          |
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            | Meta-Llama-3.1-70B-Instruct         | 70.14           | 48.768         |
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            ## Breakdown of LiveCodeBench TestOutputPrediction
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            | Model                               | Easy            | Medium         | Hard                  |
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            |-------------------------------------|-----------------|----------------|-----------------------|
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            | **Dracarys2-Llama-3.1-70B-Instruct**| **63.53**       | **47.30**      | **43.61**             |
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            | Meta-Llama-3.1-70B-Instruct         | 51.22           | 35.91          | 34.30                 |
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            ## LiveBench(Aug update)
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            | Model                               | Global Average | Coding Average | Reasoning Average| Mathematics Average | Data Analysis Average | Language Average | IF Average  |
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            |-------------------------------------|----------------|----------------|------------------|---------------------|-----------------------|------------------|-------------|
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            | **Dracarys2-Llama-3.1-70B-Instruct**| **47.8**       | **36.3**       | **47.3**         | **38.9**            | 46.1                  | 41.5             | 76.6        |
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            | Meta-Llama-3.1-70B-Instruct         | 45.1           | 30.7           | 35.3             | 37.0                | 48.4                  | 42.1             | 77.2        |
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