ms-swift

library4.3.1pypypiunverified

A scalable lightweight infrastructure for fine-tuning large language models (LLMs), vision-language models (VLMs), and embedding models. Supports LoRA, QLoRA, full fine-tuning, and reinforcement learning methods like DPO, GRPO, and PPO. Version 4.1.3 is the latest, with releases every few weeks.

ai-ml llm-agents

pip install ms-swift

github ↗package ↗

INSTALL

IMPORT

SIG · MS-SWIFT

ms-swift

ai-mlpythonv4.3.1

Install

—

Import

—

Disk

—

Pass rate

0/ 10

Env Coverage0 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v? · pip install

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.915 runs

build_error

glibc

py 3.10–3.915 runs

timeout

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

SwiftTrainer

✓ from swift import SwiftTrainer

✗ from swift.trainers import SwiftTrainer

Basic LoRA fine-tuning with Qwen2.5. Demonstrates correct imports and usage.

from swift import Swift, SwiftModel, LoRAConfig import os # Load a pre-trained model and apply LoRA model = SwiftModel.from_pretrained("Qwen/Qwen2.5-1.5B") lora_config = LoRAConfig(r=8, lora_alpha=32, target_modules=["q_proj", "v_proj"]) model = Swift.prepare_model(model, lora_config) # Quick training snippet (simplified) from swift.trainers import Seq2SeqTrainer from swift.utils import get_dataset train_dataset = get_dataset("json", data_files="train.jsonl") trainer = Seq2SeqTrainer(model=model, train_dataset=train_dataset) trainer.train()

swift --version

Debug

Known issues

breakingIn v4.0.0, the package structure was refactored. SwiftTrainer moved from `swift` to `swift.trainers`, and many utility functions moved to `swift.utils`. Code written for v3.x will break on v4.x without import adjustments.

fix

Update imports: `from swift.trainers import SwiftTrainer`, `from swift.utils import get_dataset`. Use `from swift import LoRAConfig` instead of `from swift.tuners`.

affects: >=4.0.0

deprecatedThe `Megatron` integration (Megatron-SWIFT) has been extracted to a separate repository `mcore-bridge` in v4.1.0. Future updates for Megatron training will be there.

fix

If you rely on Megatron, use the new repo: https://github.com/modelscope/mcore-bridge

affects: >=4.1.0

gotchaWhen using `Swift.prepare_model`, the `target_modules` parameter in LoRAConfig must match actual module names in the model. Common mistake: using default target_modules that may not exist, leading to no LoRA applied silently.

fix

Check model module names via `model.named_modules()` and set `target_modules` accordingly. For most models, `["q_proj", "v_proj", "k_proj", "o_proj"]` works.

affects: all

gotchams-swift requires Python >=3.8. However, some features (like GRPO) may need Python >=3.10 due to newer dependency versions. Using Python 3.8 may cause silent failures or missing optimizations.

fix

Use Python 3.10 or higher for full functionality.

affects: >=4.0

Upgrade

Version history

4.3.1latest on PyPI · released Jun 17, 2026

Audit

Dependencies

torchrequiredCore dependency for model training and inference

transformersrequiredHugging Face Transformers integration

acceleraterequiredDistributed training acceleration

datasetsrequiredDataset loading and processing

peftrequiredParameter-efficient fine-tuning (LoRA, etc.)

wandboptionalExperiment tracking (optional)

Agent activity

78 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/modelscope/ms-swift ↗

packagehttps://pypi.org/project/ms-swift/ ↗