lucidrains / lucidrains/speculative-decoding
About batch size > 1
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- Dominant language
- Python
- Stars
- 308
- Forks
- 25
- PR merge metrics
- No merged PRs in 30d
Description
First of all, thank you for open-sourcing the implementation of speculative decoding at batch size > 1. I would like to ask if it is possible to adapt directly to the models downloaded by huggingface instead of customizing their framework code. Because I tried to use this with codegen, but the generated content is messy. Hope you can answer my confusion at your convenience.
Contributor guide
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First steps
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Research direction
The issue names no files or tests. Start by tracing the repository’s speculative-decoding entry point and how it accepts models, then compare that path with Hugging Face’s model-loading path and reproduce the reported CodeGen output; done means supporting downloaded Hugging Face models at batch size >1 without framework customization.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- huggingface, python
- Domain
- ai, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100