sgl-project / sgl-project/SpecForge
[Feature] [RFC] DFlash Training Adaptation for Qwen VL Models
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- Dominant language
- Python
- Stars
- 1.2k
- Forks
- 347
- Avg merge
- 4d 1h
- Merged PRs (30d)
- 41
Description
Checklist
- 1. If the issue you raised is not a feature but a question, please raise a discussion at https://github.com/sgl-project/SpecForge/discussions/new/choose Otherwise, it will be closed.
- 2. Please use English, otherwise it will be closed.
Motivation
Adapting DFlash training for VLMs enables diffusion-based speculative decoding under multimodal inputs, thereby reducing decoding latency.
Related resources
No response
Contributor guide
No contributing guide indexed for this repository
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
No files, tests, or entry points are mentioned. The request is to adapt DFlash training for Qwen VL models, but the implementation scope and validation criteria are unspecified.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100