feat: Add ibm-granite/granite-embedding-small-english-r2 model

[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:

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• Have you added tests for your feature?
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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

Plan: Team

Run ID: 2654f9ec-5b02-40c7-9267-737dd9124856

📥 Commits

Reviewing files that changed from the base of the PR and between eca75be and 7e47ccb.

📒 Files selected for processing (1)

• fastembed/text/builtin_sentence_embedding.py

🚧 Files skipped from review as they are similar to previous changes (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 defines its output dimensions, language, license, size, ONNX source, model files, token limit, and model year. Adds canonical embedding vector values to the ONNX embedding tests.

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

Merge Risk: ⚪ Minimal · up to 7e47c

This change registers the Granite English embedding model and its canonical embedding expectations. No concrete merge-blocking risk is currently identified.

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and concisely identifies the main change: adding support for the specified IBM Granite embedding model.
Description check	✅ Passed	The description directly explains the new model support, ONNX weights, tests, canonical values, and motivation. It is related to the changeset.
Linked Issues check	✅ Passed	The PR satisfies issue #701 by registering the specified Hugging Face model with ONNX Community weights and adding canonical embedding test values. The model metadata also includes the stated context …
Out of Scope Changes check	✅ Passed	The changes are limited to model registry metadata and the corresponding canonical-value test entry. These changes support the objectives in issue #701.
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…

Full details: Linked Issues check

Explanation

The PR satisfies issue #701 by registering the specified Hugging Face model with ONNX Community weights and adding canonical embedding test values. The model metadata also includes the stated context length and release year.

Full details: Docstring Coverage

Explanation

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 files.

✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests

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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.