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Releases: modelscope/twinkle

v0.2.1

22 Apr 13:45

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中文版本

新功能

  1. 支持了Qwen/Qwen3.6-27B的魔搭官方服务,详情查看:https://www.modelscope.cn/organization/twinkle-kit

Bug修复

  1. 修复了expert权重同步错误的问题
  2. 修复了多lora场景下GRPO MoE训练崩塌的问题
  3. 修复了对多模态输入的序列切分问题
  4. 修复了pp > 1 和tp>1时服务器不正常的问题
  5. 修复了多处remote_function不正确的问题
  6. 修复了服务器训练模型上传和模型训练共用pipeline导致阻塞的问题
  7. 修复了采样器模块的一些bug

English Version

New Features

  1. Added support for the official ModelScope service on Qwen/Qwen3.6-27B. For details, see: https://www.modelscope.cn/organization/twinkle-kit

Bug Fixes

  1. Fixed an issue with incorrect expert weight synchronization.
  2. Fixed a training collapse issue with GRPO MoE in multi-LoRA scenarios.
  3. Fixed a sequence splitting issue for multimodal inputs.
  4. Fixed abnormal server behavior when pp > 1 and tp > 1.
  5. Fixed multiple incorrect remote_function implementations.
  6. Fixed a blocking issue caused by the model upload and model training pipelines sharing the same pipeline on the server side.
  7. Fixed several bugs in modules such as the Sampler.

What's Changed

Full Changelog: v0.2.0...v0.2.1

v0.2.0

14 Apr 15:48

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中文

新特性

  1. 重构了服务部分,目前的多租户服务支持tinker/twinkle双client语法规则。
  2. 支持GKD和On-policy蒸馏,请查看cookbook
  3. megatron的底层替换为mcore_bridge库,并支持了对应的多模态训练。
  4. 支持了DPO算法,请查看cookbook
  5. 支持了Qwen3.5系列的多模态任务的训练。
  6. 新增了服务端可用的Dockerfile。

Bug修复

  1. 0.2.0 bug修复较多,请查看如下的修复列表。

English

New Features

  1. Refactored the service layer; the multi-tenant service now supports both tinker/twinkle dual client syntax rules.
  2. Added support for GKD and On-policy distillation — see cookbook.
  3. Replaced the underlying Megatron backend with the mcore_bridge library, with support for corresponding multimodal training.
  4. Added support for the DPO algorithm — see cookbook.
  5. Added support for multimodal task training on the Qwen3.5 series.
  6. Added a server-side Dockerfile.

Bug Fixes

  1. A significant number of bugs have been fixed in 0.2.0 — please refer to the fix list below.

What's Changed

New Contributors

Full Changelog: v0.1.3...v0.2.0

v0.1.3

13 Mar 05:03

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中文版本

新特性

  1. 增加了client模式的便捷安装脚本,并提升了文档描述
  2. 支持transformers分支的ep+fsdp分片

Bug修复

  1. 修复加载本地数据集失败的问题
  2. 修复服务化启动时http_options错误传递到模型的问题

English Version

New features

  1. Add a shell installation script to support the client mode, and improve the description of documentation
  2. Support ep+fsdp sharding of transformers

BugFix

  1. Fix a bug that causes an error on local dataset loading
  2. Fix an error that the http_options argument was mis-transfered to the model in the server mode

What's Changed

Full Changelog: v0.1.2...v0.1.3

v0.1.2

05 Mar 15:42

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中文

新特性

  1. 支持Qwen3.5系列的transformers模型多模态训练,包含图片和视频
  2. 支持数据集预处理的batched=True操作,提升速度

Bug修复

  1. 修复NPU下权重同步卡死的问题

English

New Features

  1. Support multi-modal training of Qwen3.5 transformers framework, containing images and videos
  2. Support batched=True when preprocess datasets

BugFix

  1. Fix the hang problem of NPU weight synchronization

What's Changed

Full Changelog: v0.1.1...v0.1.2

v0.1.1

03 Mar 03:32

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Twinkle 0.1.1 version Release

中文

  • 支持Qwen3.5-2B~Qwen3.5-9B等Dense模型

English

  • Support model series of Qwen3.5-2B~Qwen3.5-9B

Full Changelog: v0.1...v0.11

v0.1

02 Mar 14:26

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中文

Twinkle框架的0.1版本发布!

新功能

  1. 🎉完整的数据集、DataLoader、Loss、Transformers和Megatron模型、Advantage、Sampler等组件的支持
  2. 🎉支持PT、SFT、RL等多种训练Stage,并支持单卡、多机多卡、Ray、Client-Server等多种训练模式
  3. 🎉支持了首版的多租户复用训练,并完整开源了server端实现。使用ray serve实现了多副本可扩缩容部署,并支持粘滞路由
  4. 🎉在魔搭官方网站上,提供了在线服务,用户可以使用该服务免费训练Qwen/Qwen3-30B-A3B-Instruct-2507,并推送模型到ModelHub上

English

Twinkle Framework Version 0.1 Released!

New Features

  1. 🎉 Full support for components including Dataset, DataLoader, Loss, Transformers and Megatron models, Advantage, Sampler, and more
  2. 🎉Support for multiple training stages such as PT, SFT, and RL, with various training modes including single-GPU, multi-node multi-GPU, Ray, and Client-Server
  3. 🎉 First version of multi-tenant shared training is now supported, with the server-side implementation fully open-sourced. Multi-replica scalable deployment is implemented using Ray Serve, with support for sticky routing
  4. 🎉 An online service is now available on the ModelScope official website, where users can train Qwen/Qwen3-30B-A3B-Instruct-2507 for free and push models to ModelHub

What's Changed

New Contributors

Full Changelog: https://github.com/modelscope/twinkle/commits/v0.1