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Model: ajibawa-2023/Python-Code-13B Source: Original Platform
2026-07-22 17:33:10 +08:00
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+---
+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         |