feat(text): support Granite 107M multilingual embeddings

[Coding-Professional]

Summary

• Register ibm-granite/granite-embedding-107m-multilingual as a built-in text model so TextEmbedding can load its upstream ONNX weights directly.
• Use the existing CLS pooling and L2 normalization path, matching the model's reference implementation (384 dimensions, 512-token limit).
• Add a canonical vector from a float32 PyTorch AutoModel run to the model regression tests.

Fixes #538

Validation

• Loaded the real ONNX model and checked English/French embeddings, 384-dimensional float32 output, unit norms, and query embedding.
• Confirmed the ONNX vector matches the independent PyTorch reference.
• Focused pytest: 9 passed; canonical vector regression passed for the new model.
• Ruff check, Ruff format check, and git diff --check passed.

The full all-model test matrix was not run locally because it downloads every supported model.

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ℹ️ Recent review info

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📥 Commits

Reviewing files that changed from the base of the PR and between 7d36728 and e0ba4c8.

📒 Files selected for processing (2)

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

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📝 Walkthrough

Walkthrough

The ONNX text model catalog now includes ibm-granite/granite-embedding-107m-multilingual, with its model dimensions, license, size, source, file name, and model description. The embedding tests now include a canonical vector for this model.

Priority: ⬇️ Low

Estimated code review effort: 2 (Simple) | ~5 minutes

Change: Feature

Suggested reviewers: joein

Merge Risk: ⚪ Minimal · up to e0ba4

Granite is registered against its published model artifact, and the canonical test covers its embedding output. No actionable merge risk was found.

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	Issue [#538] requests support for the IBM Granite multilingual embedding model. The pull request adds ibm-granite/granite-embedding-107m-multilingual to the ONNX text model catalog with model metada…
Out of Scope Changes check	✅ Passed	The changes are limited to registering the model in fastembed/text/onnx_embedding.py and adding its expected embedding vector in tests/test_text_onnx_embeddings.py. Both changes support the model …
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…
Title check	✅ Passed	The title clearly identifies the main change: support for Granite 107M multilingual embeddings. It is concise and specific.
Description check	✅ Passed	The description directly explains the model registration, implementation details, validation results, and test coverage for the changeset.

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[joein]

Hey @Coding-Professional

I think this can be used via add_custom_model, can't it?

[joein]

Thank you for your work!
Though, since we're trying to prevent our model zoo from bloating, we recommend using this model via add_custom_model interface.