[Aryan-SINGH-GIT]

Resolves #701

Description

Adds support for the ibm-granite/granite-embedding-small-english-r2 model using the ONNX Community exported weights. This is a highly requested, very lightweight embedding model that punches well above its weight class on the MTEB leaderboard.

All Submissions:

• Have you followed the guidelines in our Contributing document?
• Have you checked to ensure there aren't other open Pull Requests for the same update/change?

New Feature Submissions:

• Does your submission pass the existing tests?
• Have you added tests for your feature?
• Have you installed pre-commit with pip3 install pre-commit and set up hooks with pre-commit install?

New models submission:

• Have you added an explanation of why it's important to include this model?
• Have you added tests for the new model? Were canonical values for tests computed via the original model?
• Have you added the code snippet for how canonical values were computed?
• Have you successfully ran tests with your changes locally?

Code snippet for canonical values

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("ibm-granite/granite-embedding-small-english-r2")
embeddings = model.encode(["hello world", "flag embedding"])

print(list(embeddings[0][:5]))
# Output: [0.47021756, -0.08181943, -0.97021246, 0.10116885, -0.16487208]

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ℹ️ Recent review info

⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

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Run ID: 3c298120-b2a1-4259-88ab-cd1d172c4f69

📥 Commits

Reviewing files that changed from the base of the PR and between 7e47ccb and a50290c.

📒 Files selected for processing (1)

• fastembed/text/builtin_sentence_embedding.py

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📝 Walkthrough

Walkthrough

Adds ibm-granite/granite-embedding-small-english-r2 to the built-in sentence embedding model registry. The entry specifies its dimensions, language, token limit, prefix and normalization settings, license, size, ONNX source, and model files. Adds canonical embedding vector values for the model to the ONNX embedding tests.

Priority: ➖ Normal

Estimated code review effort: 1 (Trivial) | ~5 minutes

Change: Feature

Suggested reviewers: joein

Merge Risk: ⚪ Minimal · up to a5029

The change registers Granite and adds a canonical-vector check for the full/manual test suite; standard CI’s representative-model selection is intentional. No concrete issue currently blocks merging.

Architecture Summary

Architecture risk: 🔵 Low · up to a5029

The change affects 2 systems.

Changed systems: fastembed, tests

Architecture concerns
No architecture-level concerns identified.

Review details

Systems and components

• observed — fastembed (service) was modified; 1 changed file maps to changed impact.
• observed — tests (service) was modified; 1 changed file maps to changed impact.

Before / after behavior

• observed — Modified behavior in tests/test_text_onnx_embeddings.py: Added a CANONICAL_VECTOR_VALUES entry for ibm-granite/granite-embedding-small-english-r2 mapping it to a 5-element numpy array of expected embedding values, so the model is now covered by the embedding assertions.
• observed — Modified behavior in fastembed/text/builtin_sentence_embedding.py: Adds the Granite embedding model descriptor with its dimensions, language, token limit, prefix and normalization characteristics, license, size, source, and ONNX files.

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly identifies the addition of the IBM Granite embedding model, which is the main change in the pull request.
Description check	✅ Passed	The description explains the model addition, its ONNX Community weights, motivation, tests, and canonical-value generation.
Linked Issues check	✅ Passed	The PR satisfies issue #701. fastembed/text/builtin_sentence_embedding.py registers ibm-granite/granite-embedding-small-english-r2 with the ONNX Community source `onnx-community/granite-embedding-…
Out of Scope Changes check	✅ Passed	The changes are limited to the built-in model registry and its canonical embedding test data. Both changes directly support issue #701. No unrelated implementation, configuration, or documentation cha…
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 2…

✨ Finishing Touches

🧪 Generate unit tests (beta)

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Actionable comments posted: 1

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Inline comments:
In `@fastembed/text/builtin_sentence_embedding.py`:
- Around line 15-16: Update the Granite model metadata description in the
relevant built-in sentence embedding definition to state 8192 input tokens
truncation and the 2025 release year, matching load_tokenizer’s model_max_length
configuration.

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ℹ️ Review info

⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

Plan: Team

Run ID: 9ded1b81-5d54-4294-a597-ad5bf699e713

📥 Commits

Reviewing files that changed from the base of the PR and between a34e7bc and eca75be.

📒 Files selected for processing (2)

• fastembed/text/builtin_sentence_embedding.py
• tests/test_text_onnx_embeddings.py

Included review availability: Your plan provides up to 8 included reviews per hour; 7 remain after this review.

[joein]

@Aryan-SINGH-GIT
thank for adding this!