codablellm 1.0.6

A framework for creating and curating high-quality code datasets tailored for large language models

CodableLLM

CodableLLM is a Python framework for creating and curating high-quality code datasets tailored for training and evaluating large language models (LLMs). It supports source code and decompiled code extraction, with a flexible architecture for handling multiple languages and integration with custom LLM prompts.

Installation

PyPI

Install CodableLLM directly from PyPI:

pip install codablellm

Docker

Alternatively, you can build and run CodableLLM's CLI using Docker:

Build the image:

docker build -t codablellm .

Run the container with access to your local files:

docker run --rm -it -v $(pwd):/workspace -w /workspace codablellm \
    codablellm --url https://github.com/dmanuel64/codablellm/raw/refs/heads/main/examples/demo-c-repo.zip \
    --build "cd /tmp/demo-c-repo && make" \
    /tmp/demo-c-repo demo-c-repo.csv /tmp/demo-c-repo

This mounts your current directory to /workspace inside the container, allowing access to input/output files.

Features

• Extracts functions and methods from source code repositories using tree-sitter.
• Easy integration with LLMs to refine or augment extracted code (e.g. rename variables, insert comments, etc.)
• Language-agnostic design with support for plugin-based extractor and decompiler extensions.
• Extendable API for building your own workflows and datasets.

Documentation

Complete documentation is available on Read the Docs:

• User Guide
• Supported Languages & Decompilers
• API Reference

Contributing

We welcome contributions from the community! See CONTRIBUTING.md for guidelines, development setup, and how to get started.