OptimalScale / OptimalScale/LMFlow
[New Feature][Roadmap requested]Do you have a roadmap right now?
Open
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 8.5k
- Forks
- 822
- PR merge metrics
- No merged PRs in 30d
Description
Is your feature request related to a problem? Please describe.
I am very interested in this project, you guys did a great job. And I wonder if there is a roadmap provided?
For instance:
- Is there a plan to implement the following functionalities in this project: model compression, quantization, and pruning
- Is there a plan to support other fine-tuning algorithms, such as QLORA、freeze、p-tuning?
...
Describe the solution you'd like
A clear and concise roadmap should be helpful.
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
Start by reviewing the project's current capabilities and the requested areas: model compression, quantization, pruning, QLoRA, freeze, and p-tuning. Done would mean publishing a clear roadmap that states which functionality is planned and, where available, its expected direction or timing.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100