Lightning-AI / Lightning-AI/litgpt
Faster inference, Paged attention from vllm
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 13.7k
- Forks
- 1.5k
- Avg merge
- 15h 37m
- Merged PRs (30d)
- 1
Description
I'm having great qualitative results from Falcon finetuned with adaptersv2.
The inference is better than what I have with huggingface/peft and lora, but still slow for scaling up.
Could the ideas or code from Paged attention https://github.com/vllm-project/vllm be used to really speed up the inference with parallel sampling and larger batch sizes?
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
The issue names no files, tests, or entry points. Start by locating litgpt's inference and sampling paths, then compare them with vLLM's PagedAttention approach. Done would require an agreed implementation scope and benchmarks showing improved parallel sampling or larger batch-size performance.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- huggingface, python
- Domain
- ai, machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100