[Dylancouzon]

Moves Qdrant/constella-zero from ONNX to the numpy static path from #805. Outputs match the released int8 encoder (max abs diff 4.5e-08 on 173 texts). Potion outputs are bit-identical to main.

Changes

• Three pooling options per static model. Zero pools differently from model2vec: it keeps special tokens and [UNK], and weighs a token seen c times by sqrt(c). StaticModelDescription adds add_special_tokens, skip_unknown_tokens and sqrt_token_counts. The defaults are model2vec's, so the potion entries switch to this class with no change in behavior.
• sqrt pooling. In sqrt mode, the single-text shortcut is skipped, because it does a plain sum. The count matrix takes the square root before the weights are applied.
• int8 table. Zero's model.safetensors stores int8 rows as embeddings and their per-row scales as the existing weights tensor. The download stays at 31 MB.
• No warning for cuda=False. qdrant-client always passes it. Without this fix, moving Zero would add a warning for every qdrant-client user.
• Zero removed from the ONNX list, and one test checks Zero against its model card's pooling rule.

Throughput, Apple M5 Pro

	Single query	Batched
ONNX	30 µs	106k queries/s
numpy	26 µs	244k queries/s

Release

Merge right after https://huggingface.co/Qdrant/constella-zero/discussions/1, which adds model.safetensors. Release notes: offline users and specific_model_path copies made before that merge need to download the model again.

The static path calls encode_batch_fast, which needs tokenizers 0.20 or later. Potion models have had this limit since #805, and Zero now shares it. FastEmbed allows tokenizers>=0.15, so users on 0.15 to 0.19 must upgrade tokenizers to use Zero.

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📝 Walkthrough

The change adds Qdrant/constella-zero as a static embedding model and removes its ONNX model entry. Static model descriptions now configure special-token insertion, unknown-token handling, and square-root token-count weighting. Static embedding tokenization and pooling use these settings. A regression test checks Constella pooling against a normalized reference.

Priority: ⬇️ Low

Estimated code review effort: 3 (Moderate) | ~20 minutes

Change: Feature

Merge Risk: ⚪ Minimal · up to 14a66

No actionable issue remains from this review. The large-model regression test runs manually rather than in ordinary CI.

Pre-merge checks: 5

Check name	Status	Explanation
Title check		The title clearly identifies the primary change: running constella-zero with NumPy instead of ONNX.
Docstring Coverage		Docstring coverage is 85.71% which is sufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 7 functions across 2 files.
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.
Description check		The pull request description directly matches the changes. It explains moving constella-zero from ONNX to the NumPy static path, the new pooling options, the model format, the ONNX list removal, and t…

Full details: Description check

Explanation

The pull request description directly matches the changes. It explains moving constella-zero from ONNX to the NumPy static path, the new pooling options, the model format, the ONNX list removal, and the related test.

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Actionable comments posted: 1

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• 🪄 Fix comments: Fix CodeRabbit comments on this PR

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Review comments at @fastembed/text/static_embedding.py:
- Line 299: Update the `np.sqrt` call in the `counts` normalization path to
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⚠️ Outside diff range comments (1)

🟠 Major · Publish the compatible Constella artifact before enabling this… · static_embedding.py:83

fastembed/text/static_embedding.py:83
🩺 Stability & Availability | 🟠 Major | 🏗️ Heavy lift

Publish the compatible Constella artifact before enabling this registration.

TextEmbedding("Qdrant/constella-zero") now selects StaticTextEmbedding, which loads model.safetensors and requires embeddings and weights. The published artifact contains neither that file nor those tensors. Normal downloads can therefore fail, and specific_model_path copies containing only model.onnx are also unusable. Publish model.safetensors with the required tensors and direct row layout before release.

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Review comment at @fastembed/text/static_embedding.py at line 83:
Publish a compatible Constella artifact before enabling the
“Qdrant/constella-zero” registration: ensure the artifact includes
model.safetensors containing the required embeddings and weights tensors in the
expected direct row layout, and ensure specific_model_path copies also include
this file rather than only model.onnx.

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Outside diff comments:
Review comments at @fastembed/text/static_embedding.py:
- Line 83: Publish a compatible Constella artifact before enabling the
“Qdrant/constella-zero” registration: ensure the artifact includes
model.safetensors containing the required embeddings and weights tensors in the
expected direct row layout, and ensure specific_model_path copies also include
this file rather than only model.onnx.

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🤖 Completed: Fix pre-merge checks in PR #832 — View commit 14a664a

[AstraBert]

It looks good, and I'm all for more configurability :)
Can we rebase on top of #807 so we get tokenizers v1?

[Dylancouzon]

Can we rebase on top of #807 so we get tokenizers v1?

Any specific reason to want to wait? I tested Zero with #807's tokenizer changes and the output is still identical, so when #807 lands it only needs to keep passing add_special_tokens in its encode_batch call.