Lightning-AI / Lightning-AI/lit-llama
Sequence classification with Lit-LLaMA
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- Dominant language
- Python
- Stars
- 6.1k
- Forks
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Description
I'm trying to use Lit-LLaMA with LoRA for a sequence classification problem involving a dataset of tweets, similar to `ought/raft`. A straightforward approach would be to follow the idea described in the T5 paper, which treats the problem as a text generation task and utilizes the logits/probabilities of the class label tokens. My question is whether this approach is suitable for (Lit-)LLaMA as well, or if there is a better alternative for tackling this problem.
Contributor guide
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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
No file, test, or entry point is named. Start by locating the Lit-LLaMA LoRA fine-tuning and sequence-task entry points, then determine whether the T5-style class-label approach is supported or whether a separate classification design is needed; done requires a decided approach and corresponding implementation.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100