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## 📝 Introduction
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🍲 ms-swift is an official framework provided by the ModelScope community for fine-tuning and deploying large language models and multi-modal large models. It currently supports the training (pre-training, fine-tuning, human alignment), inference, evaluation, quantization, and deployment of over 400 large models and 100+ multi-modal large models. These large language models (LLMs) include models such as Qwen2.5, Llama3.2, GLM4, Internlm2.5, Yi1.5, Mistral, DeepSeek, Baichuan2, Gemma2, and TeleChat2. The multi-modal LLMs include models such as Qwen2-VL, Qwen2-Audio, Llama3.2-Vision, Llava, InternVL2.5, MiniCPM-V-2.6, GLM4v, Xcomposer2.5, Yi-VL, DeepSeek-VL2, Phi3.5-Vision, and GOT-OCR2.
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🍲 ms-swift is an official framework provided by the ModelScope community for fine-tuning and deploying large language models and multi-modal large models. It currently supports the training (pre-training, fine-tuning, human alignment), inference, evaluation, quantization, and deployment of 400+ large models and 150+ multi-modal large models. These large language models (LLMs) include models such as Qwen2.5, Llama3.3, GLM4, Internlm2.5, Yi1.5, Mistral, DeepSeek2.5, Baichuan2, Gemma2, and TeleChat2. The multi-modal LLMs include models such as Qwen2-VL, Qwen2-Audio, Llama3.2-Vision, Llava, InternVL2.5, MiniCPM-V-2.6, GLM4v, Xcomposer2.5, Yi-VL, DeepSeek-VL2, Phi3.5-Vision, and GOT-OCR2.
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🍔 In addition, ms-swift gathers the latest training technologies, including LoRA, QLoRA, Llama-Pro, LongLoRA, GaLore, Q-GaLore, LoRA+, LISA, DoRA, FourierFt, ReFT, UnSloth, and Liger. ms-swift supports accelerating the inference, evaluation, and deployment modules using vLLM and LMDeploy. To help researchers and developers fine-tune and apply large models more easily, ms-swift also provides a Gradio-based Web-UI interface and a wealth of best practices.
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🍔 In addition, ms-swift gathers the latest training technologies, including LoRA, QLoRA, Llama-Pro, LongLoRA, GaLore, Q-GaLore, LoRA+, LISA, DoRA, FourierFt, ReFT, UnSloth, and Liger. ms-swift supports acceleration of inference, evaluation, and deployment modules using vLLM and LMDeploy, and supports the quantization of large models and multi-modal large models using technologies such as GPTQ, AWQ, and BNB. To help researchers and developers fine-tune and apply large models more easily, ms-swift also provides a Gradio-based Web-UI interface and a wealth of best practices.
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**Why choose ms-swift?**
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- 🍎 **Model Types**: Supports 400+ large language models and **100+ multi-modal large models** and all-to-all models, **providing a comprehensive solution from training to deployment**.
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- 🍎 **Model Types**: Supports 400+ large language models and **150+ multi-modal large models** and all-to-all models, **providing a comprehensive solution from training to deployment**.
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-**Dataset Types**: Comes with 150+ pre-training, fine-tuning, human alignment, multi-modal datasets, and supports custom datasets.
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-**Hardware Support**: Compatible with CPU, RTX series, T4/V100, A10/A100/H100, Ascend NPU, etc.
Copy file name to clipboardExpand all lines: docs/source_en/GetStarted/Quick-start.md
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# Quick Start
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ms-swift is a comprehensive training and deployment framework for large language models and multimodal large models, provided by the ModelScope Community. It currently supports the training (CPT, SFT, RLHF), inference, evaluation, quantization, and deployment of over 400 LLM and over 100 MLLM. Model developers can fulfill all kinds of needs related to large models in a single platform within the ms-swift framework. The main capabilities of ms-swift include:
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ms-swift is a comprehensive training and deployment framework for large language models and multimodal large models, provided by the ModelScope Community. It currently supports the training (CPT, SFT, RLHF), inference, evaluation, quantization, and deployment of 400+ LLM and 150+ MLLM. Model developers can fulfill all kinds of needs related to large models in a single platform within the ms-swift framework. The main capabilities of ms-swift include:
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- 🍎 Model Types: Supports the full process from training to deployment of over 400 text-based large models and over 100 multimodal large models, including All-to-All all-modality models.
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- 🍎 Model Types: Supports the full process from training to deployment of 400+ text-based large models and 150+ multimodal large models, including All-to-All all-modality models.
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- Dataset Types: Comes with more than 150 pre-built datasets for pre-training, fine-tuning, human alignment, multimodal, and supports custom datasets.
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- Hardware Support: Compatible with CPU, RTX series, T4/V100, A10/A100/H100, Ascend NPU, and others.
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- 🍊 Lightweight Training: Supports lightweight fine-tuning methods like LoRA, QLoRA, DoRA, LoRA+, ReFT, RS-LoRA, LLaMAPro, Adapter, GaLore, Q-Galore, LISA, UnSloth, Liger-Kernel, and more.
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