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Fine-Tune a Smaller Open Source LLM with Devcontainer Repositories #21

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nkkko opened this issue Sep 26, 2024 · 0 comments
Open

Fine-Tune a Smaller Open Source LLM with Devcontainer Repositories #21

nkkko opened this issue Sep 26, 2024 · 0 comments

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@nkkko
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nkkko commented Sep 26, 2024

Is your feature request related to a problem? Please describe.
Current large language models (LLMs) can be resource-intensive and lack the specificity to generate high-quality devcontainer.json files for diverse repositories. Fine-tuning a smaller, open-source LLM with specific data can enhance its accuracy and efficiency.

Describe the solution you'd like

  • Fetch repositories that already have devcontainer.json files or use our db if adequate.
  • Extract relevant contexts from these repositories as described in the script.
  • Fine-tune a smaller, open-source LLM (e.g., llama, etc.) using these context-devcontainer.json pairs to improve its ability to generate high-quality devcontainer configurations.

Describe alternatives you've considered

  • Continuing to rely on larger, more generic models, but this approach may not be as effective or efficient.
  • Using pre-trained models without fine-tuning, which might not capture the specifics of devcontainer.json generation.

Additional context

  1. Data Collection:

    • Script to fetch repositories with existing devcontainer.json files.
    • Extract and format relevant context from these repositories.
  2. Model Fine-Tuning:

    • Use the collected data to fine-tune a smaller, open-source LLM.
    • Ensure the model is trained to understand context and generate appropriate devcontainer.json configurations.
  3. Implementation:

    • Integrate the fine-tuned model into the existing workflow for generating devcontainer.json files.
    • Test the model to ensure it produces accurate and useful configurations.

This approach will enhance the efficiency and accuracy of generating devcontainer files, leveraging a smaller, optimized model tailored to the specific use case.

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