modelscope / modelscope/ms-swift
🍭[Roadmap] ms-swift3.9-3.12
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- Dominant language
- Python
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Description
Models
- Integrate the latest models (P0)
- Padding-free/packing multimodal support for more models
- Hybrid data training support for more multimodal models
- Agent templates support more models
Training
- RAY supportP0
a. DPO
b. GRPO
c. PPO - Support for embedding/reranker/sequence classification
a. ✅padding_free/packing/seq_parallel - ✅Mixed use of ulysses & ring attention
- ✅Embedding & reranker dataset format refactoring
- ms-swift directory structure refactor; decoupling templates from model_type P0
Megatron-SWIFT
- New model support P0
- ✅blockwise FP8 P0
- ✅MTP support P0
- Multimodal support: ViT partial TP/CP performance optimization
- ✅GRPO P0
- RLHF support: ✅Reward Model;✅KTO;✅GKD;MPO
- More training task types supported: ✅sequence classification; embedding; reranker
- Deepspeed/fsdp2 integration
- ✅Weight conversion optimization (mcore-bridge): multi-machine conversion; GRPO compatibility; remove explicit conversion step; support direct LoRA export P0
- ✅Storage: save per epoch; save_total_limit; async saving support P0
- ✅VPP and heterogeneous PP
- Support for Megatron infer/deploy/eval modules
RL
- GRPO
a. ✅Weight sync optimization(LoRA/MoE)P0
b. ✅Training memory optimization
c. ✅CHORD
d. ✅training-inference mismatch (TIS/MIS/FP32 lm_head ...)
e. reward async/parallel execution
f. fully async rollout pipeline - ✅RLOO P0
- ✅Reinforce++ P0
- ✅KTO padding_free & packing support
- ✅liger_kernel memory optimization
- ✅GKD support for vLLM / offload
Inference & Deployment
- ✅Reranker inference & deployment support
- SGLang support for multimodal models
- vLLM/SGLang support for classification models / RM
Quantization
- Quantization support for more model types: embedding/reranker/multi-classification, etc.
- GPTQ/AWQ upgrades; ✅GPTQ-v2
ms-swift3.6-3.8 Roadmap: https://github.com/modelscope/ms-swift/issues/4561
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The roadmap is organized into Models, Training, Megatron-SWIFT, RL, Inference & Deployment, and Quantization; begin by selecting one unchecked item and reading its corresponding subsystem. No file, test, or entry point is named, so define the affected code path and an acceptance test before starting; done is the selected roadmap item implemented and verified.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- ai, distributed-systems, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100