| --- |
| license: cc-by-nc-nd-4.0 |
| datasets: |
| - ajibawa-2023/Python-Code-23k-ShareGPT |
| language: |
| - en |
| tags: |
| - code |
| --- |
| |
| **Python-Code-13B** |
|
|
| Large Language Models (LLMs) are good with code generations. Sometimes LLMs do make mistakes in code generation. How about if they can give detailed explanation along with the code. |
| This is what I have tried over here. The base Llama-2 model was used for training purpose. It is trained on around 23000+ set of codes. Each set having 2 conversations. |
| This data was generated using GPT-3.5, GPT-4 etc. This conversation is in Vicuna/ShareGPT format. Each set, along with code, has detailed explanation. |
| I have released the [data](https://huggingface.co/datasets/ajibawa-2023/Python-Code-23k-ShareGPT). |
|
|
| **Training:** |
| Entire dataset was trained on Azure 4 x A100 80GB. For 3 epoch, training took 13 hours. DeepSpeed codebase was used for training purpose. This was trained on Llama-2 by Meta. |
|
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| This is a full fine tuned model. Links for quantized models are given below. |
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|
| **GPTQ GGML & AWQ** |
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| GPTQ: [Link](https://huggingface.co/TheBloke/Python-Code-13B-GPTQ) |
|
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| GGUF: [Link](https://huggingface.co/TheBloke/Python-Code-13B-GGUF) |
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| AWQ: [Link](https://huggingface.co/TheBloke/Python-Code-13B-AWQ) |
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|
|
| **Example Prompt:** |
| ``` |
| This is a conversation with your helpful AI assistant. AI assistant can generate Python Code along with necessary explanation. |
| |
| Context |
| You are a helpful AI assistant. |
| |
| USER: <prompt> |
| ASSISTANT: |
| ``` |
| # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
| Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Python-Code-13B) |
|
|
| | Metric | Value | |
| |-----------------------|---------------------------| |
| | Avg. | 47.16 | |
| | ARC (25-shot) | 58.79 | |
| | HellaSwag (10-shot) | 81.66 | |
| | MMLU (5-shot) | 54.78 | |
| | TruthfulQA (0-shot) | 42.83 | |
| | Winogrande (5-shot) | 74.03 | |
| | GSM8K (5-shot) | 9.55 | |
| | DROP (3-shot) | 8.5 | |
|
|
