{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"ms-swift is a framework for training, evaluating, quantizing, and deploying large language models and multimodal models at scale, supporting 600+ text models and 400+ multimodal models with distributed training and inference acceleration.","skillfed_tags":["llm-training","distributed-ml","model-quantization"],"use_cases":["Fine-tune a 7B language model on custom instruction data using LoRA with only 9GB of GPU memory via quantized training.","Train a multimodal model on mixed text, image, and video data with independent control over vision encoder, aligner, and LLM components.","Deploy a trained model with OpenAI-compatible inference endpoints using vLLM or SGLang acceleration.","Run distributed training of a mixture-of-experts model across multiple GPUs using Megatron parallelism strategies.","Evaluate a fine-tuned model against 100+ standard benchmarks using the integrated EvalScope backend.","Quantize a trained model to AWQ, GPTQ, or FP8 format for reduced memory and faster inference."],"what_it_does":"ms-swift is a comprehensive framework for training and deploying large language models and multimodal models, built by the ModelScope community. It handles the full pipeline from pre-training and fine-tuning through inference, evaluation, quantization, and deployment. The framework supports a wide range of model architectures (including Qwen, InternLM, GLM, Mistral, DeepSeek, and Llama for text; and Qwen-VL, Kimi-K3, Llava, and others for multimodal) and training tasks including supervised fine-tuning, reinforcement learning algorithms (GRPO family), preference learning (DPO, KTO, SimPO), and embedding/reranker training.\n\nThe framework emphasizes efficiency and scalability through support for parameter-efficient methods (LoRA, QLoRA, DoRA, Adapter), distributed training strategies (DDP, DeepSpeed ZeRO, FSDP, Megatron), memory optimization techniques (GaLore, Flash-Attention, sequence parallelism), and quantization-aware training on BNB, AWQ, GPTQ models. It includes inference acceleration via vLLM, SGLang, and LMDeploy, provides a web UI for training and evaluation, and integrates EvalScope for model evaluation across 100+ datasets.","worth_installing":"Yes. ms-swift is actively maintained, permissively licensed, and offers a production-ready framework for LLM and multimodal model training and deployment. The low install friction, broad hardware support, and comprehensive feature set (distributed training, quantization, inference acceleration, evaluation) make it valuable for teams working with modern large models. No known security vulnerabilities. Install if you need end-to-end LLM training and deployment infrastructure; skip if you only need inference or a simpler fine-tuning wrapper."},"id":"ms-swift","links":{"html":"https://skillfed.io/packages/ms-swift","md":"https://skillfed.io/packages/ms-swift.md","pypi":"https://pypi.org/project/ms-swift/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"permissive","name":"ms-swift","python_support":"supports_current","summary":"Swift: Scalable lightWeight Infrastructure for Fine-Tuning"},"popularity":{"monthly_downloads":130605,"position":11635,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"4.5.0"}