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A Guide to Training Your Own Coding Assistant

·2023.10.27 09:00

Key point

Introduces how to build a personal coding assistant optimized for a specific codebase using Hugging Face's public repositories, along with HugCoder.

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Details

Hugging Face has released a methodology for building a Personal Copilot optimized for a specific company or individual's codebase, along with the HugCoder model trained for this purpose.

Data Collection and Processing

  • Parallel Downloading: To address GitHub API rate limit issues, multiprocessing was used to clone all public repositories locally in parallel.
  • Data Filtering: Non-code files such as images and presentations were excluded, and only code cells were extracted from Jupyter Notebooks. Unnecessary paths like .git and __pycache__ were also removed.
  • Efficient Storage: Data was serialized using the feather format to optimize memory usage.

Dataset Composition

  • The dataset was built based on Hugging Face's top 10 public repositories, including transformers, diffusers, and peft.
  • Users can apply this workflow to train a customized coding model on their own proprietary codebase.
llmcode-generationhuggingfacefine-tuningcoding-assistant
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