初始化项目，由ModelHub XC社区提供模型
Model: ajibawa-2023/Python-Code-33B Source: Original Platform
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ModelHub XC
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README.md
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README.md
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---
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language:
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- en
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license: cc-by-nc-nd-4.0
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tags:
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- code
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datasets:
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- ajibawa-2023/Python-Code-23k-ShareGPT
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model-index:
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- name: Python-Code-33B
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 56.31
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Python-Code-33B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 81.01
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Python-Code-33B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 54.22
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Python-Code-33B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 44.39
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Python-Code-33B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 75.22
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Python-Code-33B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 19.18
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Python-Code-33B
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name: Open LLM Leaderboard
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---
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**Python-Code-33B**
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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 42 hours. DeepSpeed codebase was used for training purpose. This was trained on Llama-1 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-33B-GPTQ)
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GGUF: [Link](https://huggingface.co/TheBloke/Python-Code-33B-GGUF)
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AWQ: [Link](https://huggingface.co/TheBloke/Python-Code-33B-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-33B)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |55.06|
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|AI2 Reasoning Challenge (25-Shot)|56.31|
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|HellaSwag (10-Shot) |81.01|
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|MMLU (5-Shot) |54.22|
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|TruthfulQA (0-shot) |44.39|
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|Winogrande (5-shot) |75.22|
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|GSM8k (5-shot) |19.18|
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