xtuner

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Basic Information

XTuner is an open-source toolkit designed to efficiently and flexibly fine-tune large language models (LLMs) and vision-language models (VLMs). It provides end-to-end support for pre-training, instruction fine-tuning, agent fine-tuning, chatting with models, model conversion, merging adapters, and deployment preparation. The project targets a wide range of model sizes and hardware setups, allowing fine-tuning of 7B models on a single 8GB GPU and scaling to multi-node training for models exceeding 70B. It integrates high-performance operators and can use DeepSpeed optimizations to reduce memory and speed up training. XTuner includes ready-to-use configurations and data pipelines that accommodate many dataset formats. The toolkit supports multiple training algorithms such as QLoRA, LoRA and full-parameter fine-tuning and provides utilities to convert trained checkpoints into Hugging Face format for downstream deployment and evaluation.

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Features
XTuner bundles performance optimizations, flexible configuration management, and broad model compatibility. Performance features include automatic dispatch of optimized operators like FlashAttention and Triton kernels, sequence-parallel training, and DeepSpeed ZeRO integration for memory efficiency and throughput. The CLI exposes commands for listing and copying configs, starting training with xtuner train, chatting with models, and converting or merging adapters into deployable models. XTuner supports many model families and VLM architectures out of the box, and ships example configs for QLoRA, DPO, ORPO, reward modeling, and other algorithms. The project also provides a robust data pipeline that can handle a variety of datasets and system contexts, compatibility with synthetic data generation tools for dataset creation, and interoperability with evaluation and deployment tools through standardized model outputs.
Use Cases
XTuner helps researchers and engineers reduce the barrier to fine-tuning large models by providing performance optimizations and scalable training recipes that work on constrained hardware as well as multi-node clusters. It shortens iteration cycles with ready-made configs, an extensible data pipeline, and CLI utilities for training, chatting, converting checkpoints, and merging adapters. By supporting popular algorithms like QLoRA and LoRA and enabling DeepSpeed optimizations and sequence parallelism, XTuner lowers GPU memory requirements while improving throughput. Its conversion tools produce Hugging Face compatible artifacts for easier deployment and integration with model serving and evaluation frameworks. The toolkit also supports multi-modal VLM workflows and is useful for building, evaluating, and deploying fine-tuned models across research and production scenarios.

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