deepsweet / deepsweet/mlx-eval
Enhanced quantization (oQe)
- Dominant language
- Python
- Stars
- 26
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Description
This might be a really silly question, however I notice that you haven't included any of the Enhanced quantization (oQe) levels.
My understanding from [https://github.com/jundot/omlx/blob/main/docs/oQ_Quantization.md](https://github.com/jundot/omlx/blob/main/docs/oQ_Quantization.md) is that these enhanced quants add more precision, especially to the lower quants.
Do you plan to include these in the future, or have I misunderstood what oQe is?
Contributor guide
No contributing guide indexed for this repository
Research direction
Start by reading the linked docs/oQ_Quantization.md to understand the oQe levels and how they differ from the quantizations currently evaluated. Then inspect mlx-eval's quantization-related entry points and document the required scope; this issue is done only when the supported levels and evaluation expectations are agreed.
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
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100