Introducing XTuner: The Versatile Fine-Tuning Toolkit
XTuner is an innovative and powerful toolkit designed to fine-tune large models like language models (LLMs) and vision-language models (VLMs). It emphasizes efficiency, flexibility, and comprehensive functionality, making it an invaluable resource for developers looking to optimize AI models for specific tasks or datasets.
Efficiency at Its Core
XTuner is engineered to fine-tune models efficiently on a wide range of GPUs, from single 8GB GPUs for smaller models to extensive multi-node setups for models exceeding 70 billion parameters. The use of advanced performance tools like FlashAttention and Triton kernels ensures optimal training throughput. Furthermore, XTuner is compatible with DeepSpeed, a powerful tool from Microsoft that leverages various optimization techniques to further enhance performance.
Flexibility for Diverse Applications
XTuner supports a broad spectrum of models, including well-known LLMs like InternLM, Llama, and ChatGLM, as well as VLMs such as LLaVA. Its versatile data pipeline can handle datasets in a variety of formats, whether they are open-source or custom-created, giving users immense flexibility in model training. Additionally, XTuner offers several training algorithms, including QLoRA, LoRA, and full-parameter fine-tuning, allowing for tailored solutions that meet specific project requirements.
Rich Functionality
With XTuner, users can engage in continuous pre-training, instruction fine-tuning, and agent-specific fine-tuning. It also facilitates interactions with large models using predefined templates and ensures seamless integration with deployment tools like LMDeploy and evaluation frameworks like OpenCompass. This full-featured approach means users can handle the entire model lifecycle from training to deployment and evaluation without switching between different tools.
Key Features and Support
- Model Support: Featuring a wide array of models such as InternLM, Llama, and Phi-3, XTuner is equipped to deal with various model types and architectures.
- SFT Datasets: It accommodates numerous datasets like MSAgent-Bench and Alpaca, among others.
- Data Pipelines and Algorithms: The toolkit supports complex pipelines and a range of algorithms including QLoRA and LoRA, tailored to enhance the training process.
- Training and Chat: XTuner supports efficient fine-tuning, offering guides to prepare datasets and start the training process with predefined configurations. Its chat feature allows interactive sessions with trained models.
Getting Started with XTuner
Installation: Install XTuner in a Python 3.10 virtual environment via conda or pip. For those integrating with DeepSpeed, an additional installation step is required.
Fine-Tuning: Prepare the desired configuration by selecting or modifying a provided template. Begin fine-tuning using a single command for ease of deployment, with options to optimize training using DeepSpeed.
Deployment and Evaluation: Convert your trained models for deployment and leverage tools like LMDeploy for efficient usage. For comprehensive evaluations, utilize OpenCompass to assess model performance across diverse datasets.
Join the Community
Whether you are already working with XTuner or thinking about trying it out, the project welcomes contributions from developers worldwide. Join the community on platforms like GitHub, and engage with a network of innovators and experts who are advancing the capabilities of large model fine-tuning.
Acknowledgments and Licensing
XTuner draws inspiration and support from leading projects in the field like the Llama model and DeepSpeed. Released under the Apache License 2.0, XTuner adheres to legal requirements while encouraging innovation and collaboration in AI development.
For those looking to take their model fine-tuning capabilities to the next level, XTuner offers an exciting suite of tools tailored to deliver exceptional results efficiently and flexibly.