Is the accuracy reported in the paper the result with finetuning applied?
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- Dominant language
- Python
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Description
I have the following questions.
I know you must be busy, but I would truly appreciate it if you could find the time to respond.
- Are the perplexity and accuracy results reported in Tables 2, 3, 4, 5, and 6 of the paper obtained after finetuning?
- If so, I would like to know whether it is end-to-end or layer-wise finetuning.
- Additionally, I am not sure how to implement the finetuning step after vector quantization in code, so I would greatly appreciate any hints or guidance on this.
- Finally, if you have any insight into the difference in effectiveness between LoRA finetuning and the end-to-end or layer-wise finetuning described in the paper, I would be grateful if you could share that as well.
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Research direction
Begin with the paper's Tables 2–6 and the repository's vector-quantization workflow. Determine whether the reported metrics use fine-tuning and whether it is end-to-end or layer-wise, then document the post-quantization fine-tuning guidance and the requested comparison with LoRA. No files or tests are identified in the issue.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100