BigCodeLLM-FT-Proj: A Comprehensive Fine-Tuning Framework for Large Code Models

BigCodeLLM-FT-Proj is a comprehensive fine-tuning framework for large code language models, covering the entire workflow of data preparation, training configuration, evaluation, and deployment. It supports multiple model architectures (such as GPT series, CodeLlama, CodeT5) and fine-tuning strategies (LoRA, QLoRA, etc.), aiming to lower the threshold for code model fine-tuning and facilitate the construction of domain-specific code AI assistants.

General pre-trained code models need further adaptation for specific programming languages, internal enterprise codebases, or task types. BigCodeLLM-FT-Proj provides an end-to-end solution covering the entire workflow of data preprocessing, training configuration, evaluation and validation, and deployment. Its goal is to lower the threshold for fine-tuning, enabling developers to quickly build domain-specific code AI.

Modular Architecture

Layered design: Data layer (collection and cleaning), preprocessing layer (tokenization and parsing), training layer (distributed training), evaluation layer (assessment), deployment layer (export). Each stage can be used independently or in series.

Multi-Model Support

Compatible with Decoder-only (GPT, CodeLlama), Encoder-Decoder (CodeT5), MoE (Mixtral) architectures, with abstract interfaces to mask differences.

Fine-Tuning Strategies

Implements full-parameter fine-tuning, LoRA, QLoRA, Prefix Tuning, and Adapter Layers; users can choose based on hardware or requirements.

Data Processing

Syntax parsing and filtering, code normalization, deduplication and decontamination, instruction data construction (natural language to code, etc.), and data augmentation (semantic transformation, synthetic data).

Training Optimization

Integrates DeepSpeed/FSDP distributed training, gradient clipping, learning rate scheduling, and hyperparameter auto-search (grid/random/Bayesian optimization).

Evaluation System

• Automated evaluation: HumanEval/MBPP, CodeBLEU, Pass@k, execution success rate;
• Domain-specific evaluation: Custom evaluation sets for private codebases;
• Manual evaluation: Multi-dimensional scoring (correctness, readability, etc.) and result statistics.

Model Export and Quantization

Supports HuggingFace, GGUF, ONNX, TensorRT formats, as well as INT8/INT4, GPTQ/AWQ quantization algorithms.

Inference Services

Provides RESTful API, gRPC, streaming generation, dynamic batching, and other service tools.

• Hardware constraints: Choose QLoRA for consumer GPUs; choose full-parameter fine-tuning if data is sufficient;
• Scenario adaptation: Use private codebases for enterprise internal assistants; target niche languages (e.g., Solidity) for specific language models;
• Flexible usage: Use only the data layer to process your own codebase, or skip training to directly adapt pre-trained models.

Integrates best practices for code fine-tuning, provides reproducible configuration management, and has extensible interfaces for community contributions. Complete documentation and examples help beginners get started, providing a reliable starting point for researchers and engineers, and promoting the democratization of code AI technology.

BigCodeLLM-FT-Proj significantly lowers the threshold for building domain-specific code AI through its modular architecture, diverse fine-tuning strategies, complete training optimization, and comprehensive evaluation system. It is an important infrastructure to promote the popularization of code generation AI.