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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/Code-74k-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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**Code-33B** |
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Large Language Models (LLMs) are good with code generations. Sometimes they 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-1 model was used for training purpose. It is trained on around 74000 set of codes. Each set having 2 conversations. |
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Along with Python, Java, JavaScript, GO, C++, Rust etc. code with detailed explanation is used for training purpose. It is built upon using my existing Dataset [Python-Code-23k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Python-Code-23k-ShareGPT). |
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This conversation is in Vicuna/ShareGPT format. Each set, along with code, has detailed explanation. |
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I have released the new data [Code-74k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-74k-ShareGPT) on which this Model is trained. |
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**Training:** |
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Entire dataset was trained on Azure 4 x A100 80GB. For 3 epoch, training took 6 days & 5 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 will be updated soon. |
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**GPTQ GGUF & AWQ** |
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GPTQ: TBA |
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GGUF: TBA |
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AWQ: TBA |
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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 Code in various Programming Languages 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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You can modify above Prompt as per your requirement. I have used ShareGPT/Vicuna format v1.1 . |
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I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development. |
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Thank you for your love & support. |
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