Firefly is an open-source framework designed to simplify the training and fine-tuning of large language models through a unified and configurable workflow. The project provides a comprehensive environment where developers can perform tasks such as model pre-training, instruction tuning, and preference optimization using widely adopted machine learning techniques. Its architecture supports both full-parameter training and parameter-efficient strategies like LoRA and QLoRA, making it suitable for environments with limited computational resources. Firefly is compatible with a wide range of popular open-source models including LLaMA, Qwen, Baichuan, InternLM, and Mistral, enabling developers to experiment with different architectures using a consistent training pipeline. The framework also provides curated datasets and training templates that help streamline the process of instruction tuning and conversational model development.
Features
- Support for pre-training, instruction tuning, and preference optimization workflows
- Compatibility with many major open-source LLM architectures
- Parameter-efficient training methods such as LoRA and QLoRA
- Configuration-based training pipelines for easier experimentation
- Integration with curated instruction-tuning datasets
- Optimized training workflows designed to reduce GPU memory usage