[Model]: support Alibaba-NLP/gte-Qwen2-7B-instruct as re-rank model
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- Dominant language
- Python
- Stars
- 3.2k
- Forks
- 248
- Avg merge
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- Merged PRs (30d)
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Description
Which model would you like to support?
Alibaba-NLP/gte-Qwen2-7B-instruct
intfloat/e5-mistral-7b-instruct
What are the main advantages of this model?
Hi,
I am really impressed by the speed of the cross-encoding model.
Can you please add more powerfull models (like gte-Qwen2-7B-instruct, or, in intfloat/e5-mistral-7b-instruct) in TextCrossEncoder module?
I can contribute, if how-to.md exist, that can guide me through the process.
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 by locating the TextCrossEncoder module and reviewing how existing re-rank models are supported. Check for related tests or model-registration entry points; done means the requested Alibaba-NLP/gte-Qwen2-7B-instruct and intfloat/e5-mistral-7b-instruct models are supported there.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100