[Model]: Multi-modal embedding: Alibaba-NLP/gme-Qwen2-VL-2B-Instruct
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- Dominant language
- Python
- Stars
- 3.2k
- Forks
- 248
- Avg merge
- 4d 8h
- Merged PRs (30d)
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Description
Which model would you like to support?
Hey!
Qwen & Alibaba has open sourced a whole bunch of amazing multimodal models. These models have a ton of potential and could bring about some really cool advancements.
So, I'd really appreciate it if you could spare a bit more attention for them.
Especially, the GME and GTE series are pretty special. They might just hold the key to some exciting new developments. Have a look and see what you think!
https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct
https://huggingface.co/Alibaba-NLP/gte-modernbert-base
What are the main advantages of this model?
Multi-modal Retrieval Augmented Generation (RAG) has turned into a super hot new area lately. It's really making waves in the field!
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
Start with the linked Alibaba-NLP/gme-Qwen2-VL-2B-Instruct model page and review how fastembed currently supports embedding models. Confirm the required multimodal behavior and define completion as support for this model in the library, with validation against the model's stated retrieval use case.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- huggingface, python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100