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@@ -15,6 +15,12 @@ base_model: ajibawa-2023/Python-Code-13B
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  This model was converted to GGUF format from [`ajibawa-2023/Python-Code-13B`](https://huggingface.co/ajibawa-2023/Python-Code-13B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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  Refer to the [original model card](https://huggingface.co/ajibawa-2023/Python-Code-13B) for more details on the model.
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  ## Use with llama.cpp
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  Install llama.cpp through brew (works on Mac and Linux)
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  This model was converted to GGUF format from [`ajibawa-2023/Python-Code-13B`](https://huggingface.co/ajibawa-2023/Python-Code-13B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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  Refer to the [original model card](https://huggingface.co/ajibawa-2023/Python-Code-13B) for more details on the model.
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+ ---
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+ Model details:
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+ -
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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. 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.
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+
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+ ---
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  ## Use with llama.cpp
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  Install llama.cpp through brew (works on Mac and Linux)
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