ms-swift

ms-swift is an open-source large model framework that supports training, inference, evaluation, quantization, and deployment.

ms-swift is a large model and multimodal large model fine-tuning and deployment framework provided by the ModelScope community. It supports training, inference, evaluation, quantization, and deployment for hundreds of text-only and multimodal large models. It integrates the latest training technologies, including Megatron parallelism techniques and numerous reinforcement learning algorithms like GRPO. The framework supports a wide range of training tasks such as preference learning, embedding, reranker, and sequence classification. It provides full-pipeline support for large model training, including acceleration modules and model quantization technologies.

ms-swift at a glance

Best for: Software engineer, Researcher
Runs on: Linux, self-hosted, open source
Pricing: Free
Released: August 2023
Category: AI coding tool
Website: github.com/modelscope/ms-swift

Key features

Supports 600+ text-only and 400+ multimodal models
Integrates Megatron parallelism techniques
Provides GRPO family reinforcement learning algorithms
Supports lightweight fine-tuning methods like LoRA and QLoRA
Includes built-in datasets for various training tasks
Supports inference acceleration with vLLM, SGLang, and LMDeploy
Provides a Web-UI interface for the full pipeline

Use it for

Fine-tuning text and multimodal large models
Running reinforcement learning tasks with GRPO
Quantizing and deploying large language models
Evaluating model performance with various datasets

Alternatives to ms-swift

LMDeploy — Accelerates inference with high-performance engines and quantization tools.
AirLLM — Runs massive 70B+ parameter models on small 4GB consumer GPUs.
XTuner — High-efficiency training for 200B+ scale MoE models without expert parallelism.
MNN — It delivers high-performance inference and training with a lightweight footprint.
Unsloth — Runs and trains models locally with lower memory usage.
GLiNER — Enables zero-shot entity extraction without labeled training data on consumer CPUs.
LMFlow — It offers memory-efficient training and full Accelerate support for large language models.
MLflow — Complete observability and evaluation for production AI apps.
FastMCP — The standard framework for building Model Context Protocol servers and clients.
vLLM — Maximizes throughput with PagedAttention and continuous batching.
MTPLX — It runs local models twice as fast via native MTP speculative decoding.
FastMCP TS — Provides an open-source TypeScript framework for Model Context Protocol servers.

All ms-swift alternatives