56 lines
2.1 KiB
Markdown
56 lines
2.1 KiB
Markdown
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---
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license: cc-by-nc-nd-4.0
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datasets:
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- ajibawa-2023/Python-Code-23k-ShareGPT
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language:
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- en
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tags:
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- code
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---
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**Python-Code-13B**
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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.
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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.
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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.
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I have released the [data](https://huggingface.co/datasets/ajibawa-2023/Python-Code-23k-ShareGPT).
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**Training:**
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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:**
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```
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This is a conversation with your helpful AI assistant. AI assistant can generate Python Code along with necessary explanation.
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Context
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You are a helpful AI assistant.
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USER: <prompt>
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ASSISTANT:
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```
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Python-Code-13B)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 47.16 |
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| ARC (25-shot) | 58.79 |
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| HellaSwag (10-shot) | 81.66 |
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| MMLU (5-shot) | 54.78 |
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| TruthfulQA (0-shot) | 42.83 |
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| Winogrande (5-shot) | 74.03 |
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| GSM8K (5-shot) | 9.55 |
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| DROP (3-shot) | 8.5 |
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