ibm-granite/granite-embedding-small-english-r2
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 3.2k
- Forks
- 248
- Avg merge
- 4d 8h
- Merged PRs (30d)
- 4
Description
Which model would you like to support?
HuggingFace Link: https://huggingface.co/ibm-granite/granite-embedding-small-english-r2
ONNX Weights Link: https://huggingface.co/onnx-community/granite-embedding-small-english-r2-ONNX
What are the main advantages of this model?
It is a highly popular, lightweight embedding model that performs exceptionally well on the MTEB leaderboard for its tiny size (under 50M parameters). It's a great fit for FastEmbed's goal of fast, serverless embeddings.
Note: I have already implemented this, added it to the builtin sentence embeddings registry, and verified the canonical test values locally. I will be opening a Pull Request to address this immediately!
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 at the builtin sentence embeddings registry and review the existing support entries for the linked Hugging Face model. Run the canonical embedding test values to confirm the granite-embedding-small-english-r2 integration matches the expected results.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- huggingface, python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100