Launching Embedding Module Separately Due to Insufficient VRAMs
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Description
I've been going through the FastChat [documentation](https://github.com/lm-sys/FastChat/blob/main/docs/langchain_integration.md) and noticed the command to launch chat-completion/completion/embeddings using:
`python3 -m fastchat.serve.model_worker --model-names "gpt-3.5-turbo,text-davinci-003,text-embedding-ada-002" --model-path lmsys/vicuna-7b-v1.3`
However, due to VRAM limitations, I'm experiencing difficulties running the entire stack simultaneously. I'm interested in finding a way to solely launch the embedding module, independent of the language model.
Could you guide me on how to perform this operation? Your assistance is greatly appreciated.
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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
Start with docs/langchain_integration.md and the documented fastchat.serve.model_worker command. Check whether the existing serving entry point supports launching only the embedding module with reduced VRAM usage; done means the standalone procedure is documented clearly.
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Assessment
- Tech stack
- python
- Domain
- ai, documentation
- Issue type
- Documentation
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100