| | --- |
| | 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. |
| |
|
| | This is a full fine tuned model. Links for quantized models are given below. |
| |
|
| |
|
| | **GPTQ GGML & AWQ** |
| |
|
| | GPTQ: [Link](https://huggingface.co/TheBloke/Python-Code-13B-GPTQ) |
| |
|
| | GGUF: [Link](https://huggingface.co/TheBloke/Python-Code-13B-GGUF) |
| |
|
| | AWQ: [Link](https://huggingface.co/TheBloke/Python-Code-13B-AWQ) |
| |
|
| |
|
| | **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 | |
| |
|
