SWIFT

license

Apache-2.0(OSI)

source

public

maintainer

ModelScope(Alibaba)

core features withheld

no

Apache-2.0, an OSI license, with the full source of the training and deployment framework public and nothing held back for a paid tier.

• https://github.com/modelscope/ms-swift recorded 2026-08-08

  Repository tree lists swift/, setup.py, requirements/, tests/, docs/ and LICENSE, and the README install section gives 'git clone https://github.com/modelscope/ms-swift.git / cd ms-swift / pip install -e .', so the running framework builds from the published repo. The README License section reads 'This framework is licensed under the Apache License (Version 2.0)' and the sidebar Resources block lists 'Apache-2.0 license'. The README documents the complete pipeline (swift pt / sft / rlhf / infer / app / deploy / sample / eval / export, plus 'swift web-ui') with no paid, premium, enterprise or commercial edition; the only 'Enterprise', 'Premium' and 'Pricing' strings on the page sit in GitHub's own site navigation above the repository header, not in project content. Header shows 3,549 commits, 15.1k stars, 1.6k forks, no archive notice.

• https://pypi.org/project/ms-swift/ recorded 2026-09-23

  Project page for 'ms-swift 4.4.2', summary 'Swift: Scalable lightWeight Infrastructure for Fine-Tuning', author 'DAMO ModelScope teams'. Meta block gives License: 'Apache Software License (Apache License 2.0)' and the Classifiers block lists 'License :: OSI Approved :: Apache Software License'. The rendered long description is the repository README verbatim (same Table of Contents, same License section linking modelscope/ms-swift/blob/master/LICENSE), so the distributed package is the same framework the repo publishes.

• https://raw.githubusercontent.com/modelscope/ms-swift/main/LICENSE recorded 2026-09-23

  11,357 bytes of unmodified Apache License 2.0 text: opens 'Apache License / Version 2.0, January 2004 / http://www.apache.org/licenses/' and closes with the stock appendix carrying the unfilled placeholder 'Copyright [yyyy] [name of copyright owner]'. No custom copyright line, no added use restriction, no field-of-use rider, so the OSI call is clean rather than depending on the GitHub classifier.

• https://swift.readthedocs.io/en/latest/ recorded 2026-09-23

  Full public documentation table of contents: Get Started (SWIFT Installation with 'Source Code Installation', Quick Start, Web-UI); Instruction (Command Line Parameters, Pre-training and Fine-tuning, GRPO, Knowledge Distillation, RLHF, Inference and Deployment, Sampling, Evaluation, Export and Push, Ray Support, Reinforced Fine-Tuning, Agent Support, Supported Models and Datasets, Using Tuners, FAQ); Megatron-SWIFT; Customization; Best Practices. A case-insensitive scan of the rendered page returns zero occurrences of 'enterprise', 'premium', 'pricing', 'commercial' or 'paid' - every documented capability is documented for the open package, with no tier withheld.

Adoption is measured by PyPI downloads of ms-swift, the project's only published package.

• https://pypistats.org/api/packages/ms-swift/recent recorded 2026-10-07

  49,651 downloads in the trailing 30 days for ms-swift (pypistats last_month)

SWIFT covers full fine-tuning, LoRA, QLoRA and DoRA, the GRPO family of reinforcement learning methods, preference tuning and pretraining across more than 600 text models, with quantization, evaluation and deployment built into one pipeline. It borrows its large-scale parallelism from Megatron-LM through the Megatron-SWIFT backend rather than developing its own, which places it a step below Megatron-LM.

• https://raw.githubusercontent.com/modelscope/ms-swift/main/README.md recorded 2026-09-28

  README says ms-swift supports "training (pre-training, fine-tuning, human alignment), inference, evaluation, quantization, and deployment for 600+ text-only large models" and lists "LoRA, QLoRA, DoRA, LoRA+, LLaMAPro, LongLoRA, LoRA-GA, ReFT, RS-LoRA, Adapter, LISA", the GRPO family "GRPO, DAPO, GSPO, SAPO, CISPO, CHORD, RLOO, Reinforce++", preference methods "DPO, KTO, RM, CPO, SimPO, ORPO", and "Megatron parallelism techniques such as TP, PP, CP, EP to accelerate training"; evaluation uses EvalScope with "100+ evaluation datasets" and quantization export covers "AWQ, GPTQ, FP8, and BNB".

• https://swift.readthedocs.io/en/latest/ recorded 2026-09-28

  Documentation index has a Megatron-SWIFT section whose first page is "Megatron-SWIFT/Quick-start.html", plus "Knowledge Distillation", "Ray Support" and "Reinforced Fine-Tuning" sections, corroborating the README feature set with worked workflow pages.