questions about LLM and embedding models
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 39.5k
- Forks
- 4.8k
- PR merge metrics
- No merged PRs in 30d
Description
'''---python3 -m fastchat.serve.model_worker --model-names "gpt-3.5-turbo,text-davinci-003,text-embedding-ada-002" --model-path lmsys/vicuna-7b-v1.5
Q1:Are the 'embedding model' and 'LLM model' in this context both referring to the 'lmsys/vicuna-7b-v1.5' model?
Q2: If so, what is the reason for doing this? Can the 'm3e-base' embedding model also be loaded, and does it support as well?
Contributor guide
No contributing guide indexed for this repository
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
Review the fastchat.serve.model_worker command and the referenced image first. Trace how --model-names and --model-path are interpreted, then check whether embedding models such as m3e-base are supported. Done means documenting answers to both questions or recording that the requested setup is unsupported.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- ai, machine-learning
- Issue type
- Documentation
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100