[AutoDeploy]: test and improve the performance of quantized models
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AutoDeploy
- Dominant language
- Python
- Stars
- 14.7k
- Forks
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- Avg merge
- 2d 23h
- Merged PRs (30d)
- 489
Description
🚀 The feature, motivation and pitch
Example: nvidia/Llama-3.1-70B-Instruct-FP8 performs much worse compared to meta-llama/Meta-Llama-3.1-70B-Instruct (world=8)
Alternatives
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Additional context
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Research direction
Start by reproducing the performance comparison between nvidia/Llama-3.1-70B-Instruct-FP8 and meta-llama/Meta-Llama-3.1 using world=8. Identify the relevant quantized-model benchmark or AutoDeploy entry point, then define completion as documented testing and a measurable performance improvement; the issue does not name files or tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100