Introduction
LMFlow is an extensible toolbox for fine-tuning and inference of large foundation models. It provides end-to-end support from dataset preparation, fine-tuning and benchmarking to deployment, and ships with templates, model zoo, and reproducible examples.
Key features
- Multiple tuning methods (Full FT, LoRA, QLoRA, LISA) and support for custom optimizers.
- Acceleration and memory optimizations: Flash Attention, vLLM, DeepSpeed, gradient checkpointing, position interpolation.
- Model zoo, benchmark tools and Colab-ready tutorials for reproducible experiments.
Use cases
- Research and reproducible fine-tuning pipelines and benchmarking.
- Memory-efficient fine-tuning in constrained environments using LISA/QLoRA.
- Deploying fine-tuned models as chat or inference services with Gradio and vLLM integrations.
Technical details
- Primarily Python-based with extensive documentation at https://optimalscale.github.io/LMFlow/ .
- Supports loading datasets from Hugging Face, S3, and other sources; provides installation scripts for multiple environments.
- Licensed under Apache-2.0; actively maintained with community contributions.