new: add minish models

[stephantul]

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• 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 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?
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This PR adds support for three minishlab models:

• potion-base-8m
• potion-retrieval-32m
• potion-multilingual-128m

I put these in separate onnx exports, so it doesn't clutter up our main repositories. As discussed, these models are directly loaded as text embedders. They don't have post-processors, so special tokens do not get added. The test script below shows all lines are close to the original model counterparts. When run on this branch, it just works out of the box.

import numpy as np
from model2vec import StaticModel

from fastembed import TextEmbedding

models = [
    "minishlab/potion-base-8m",
    "minishlab/potion-retrieval-32m",
    "minishlab/potion-multilingual-128m",
]
lines = """Link: Don't worry, I'll handle this! (Link hits branch and Zelda falls)
Link: Ha ha ha. (tree branch grabs Link)
Zelda: Ha ha ha. I suppose I better get you loose. I guess.
Link: Hurry up, I'm getting squashed! (Zelda frees him with an arrow, then Link and Zelda fall down a trap)
Spryte: Link, Zelda, wait!
(at Ganon's lair)
Ganon: There, the two Triforces side by side. Nothing stands in my way now! (hears Link and Zelda fall) Hmm...we have visitors.
Zelda: Some shortcut. (Zelda and Link are trapped in a large glass container)
Ganon: Well, what have we here?
Link: Ganon! (shoots a sword beam but bounces around the glass container)
Ganon: Very good, Link. Do you know any other tricks?
Link: Lemme out of here and we'll see who laughs last.
Ganon: Strong enough to hold even the most impetuous hero. Too bad you won't be around long enough to appreciate its finest qualities. (whistles and calls a Gohma from one of the cells) Dinner time!
Link: Stay down, I'll handle this.
Zelda: Where have I heard that before...
(Gohma tries to attack the heroes but the glass container is in the way)
Ganon: No, Gohma, no! Reach down from the top! (Gohma reaches down from the top and grabs Link's sword)
Link: Let go, or I'll make crab cakes out of you!
Spryte: Link's in trouble! Maybe my magic will do something! (shoots a beam that causes Gohma to let Link out of container)
(Link shoots various sword beams at Ganon) """.splitlines()


for name in models:
    static = StaticModel.from_pretrained(name)
    fast = TextEmbedding(model_name=name)

    for line in lines:
        x = static.encode(line)
        y = list(fast.embed(line))[0]
        assert np.allclose(x, y)

    x = static.encode(lines)
    y = list(fast.embed(lines))

It tests both batch and single lines.

I also added the canonical values to the tests.

[coderabbitai]

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📝 Walkthrough

Walkthrough

The ONNX embedding registry now includes Potion Base 8M, Potion Retrieval 32M, and Potion Multilingual 128M. Each entry defines model dimensions, metadata, license, size, source, and ONNX filename. Tests include canonical five-element vectors for all three models. Existing imports were reordered.

Estimated code review effort: 2 (Simple) | ~10 minutes

Merge Risk: ⚪ Minimal · up to d58c9

This PR adds three embedding models through the existing registry and provides their expected test vectors. No actionable merge-blocking risk remains beyond normal checks and review.

Suggested reviewers: joein

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title identifies the main change: adding MinishLab models. It is concise and related to the changeset, although it does not name the specific Potion models.
Description check	✅ Passed	The description clearly explains the addition of three MinishLab models, their ONNX exports, and the related tests.
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…
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
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[Dylancouzon]

Added some inline comments, just minor nits on naming and descriptions conventions.

Verified all three locally against model2vec 0.9.0. The canonical test embeddings were generated with StaticModel.encode., and the scores match StaticModel.encode. A single embedding is bit-identical to the same text embedded in a mixed-length batch. tests/test_text_onnx_embeddings.py::test_embedding passes locally. The sizes, licenses, and file sets of all three ONNX repos check out.

@stephantul FastEmbed and StaticModel.encode still disagree in two places:

Truncation: StaticModel.encode defaults to max_length=512, while the ONNX repos set model_max_length: 32768, which is where FastEmbed truncates. On a 1,777-token document, this produces a cosine similarity of 0.984. Passing max_length=100000 to model2vec reduces the difference to 1.5e-8, confirming that truncation accounts for the entire discrepancy.

At 32,768 tokens, a full 256-document batch requires 8.6 GB, compared with 134 MB at 512. Was 32,768 deliberate?

UNK tokens: model2vec excludes UNK from pooling; the graph does not. This affects the English models only: Japanese text gives cosine 0.996 and 0.988, while all-emoji input returns zeros from model2vec and the UNK vector from FastEmbed. The canonicals contain no UNKs, so tests are unaffected. For exact parity, mask input_ids == unk_token_id during export. - This is acceptable discrepancy imo if the fix is expensive.

Otherwise, Implementation lgtm. Once those 2 things are figured out, I'll have a maintainer double validate and run the CIs.

Thanks a lot for this contribution @stephantul, I'd love to publish an article about this once this is live, let me know if you want to collaborate on it.

[stephantul]

Hey @Dylancouzon!

The 32k was deliberate. The models themselves have no hardcoded limit. For model2vec itself we use 512 as a basic length, but this can be a bit short for some tasks (most notably some NanoBEIR retrieval tasks). I can lower it to 512 for consistency if you want. In model2vec it's more of a sane defaults thing, where we don't want to change it to not break users' experiences.

The [UNK] token is a good point. I'll add it to the pipeline, and will re-upload

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Actionable comments posted: 1

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Inline comments:
In `@fastembed/text/onnx_embedding.py`:
- Around line 208-213: Update the description for the model entry named
minishlab/potion-multilingual-128M to say “Multilingual” instead of “English,”
leaving the remaining metadata unchanged.

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[Dylancouzon]

That makes sense; I think the 32k could be documented as a difference if you consider it the best value to ship.

Let me know when the UNK fix is in :)

[stephantul]

@Dylancouzon I uploaded the new models. I chose 512 as the maximum length in the end. It makes sense to me to do not diverge from the defaults in model2vec itself and not cause unnecessary memory pressure. The unk token should be fixed.

[Dylancouzon]

@stephantul Re-verified against the re-uploaded models on a clean cache. All twelve inputs I tried now match StaticModel.encode to 3e-8, including Japanese, emoji-only, "fastembed is fast 🚀", and an 1800-token document. The tokenizer reports max_length: 512. The 8M and 32M graphs now include the UNK masking but the 128M kept the old pipeline, is that expected?

One last issue remain

The test canonical keys are still lowercase while the entries now use capital M. CANONICAL_VECTOR_VALUES[model_desc.model] raises KeyError: 'minishlab/potion-base-8M' at line 176. That one is on me, my suggestions renamed the entries and left the keys behind. Please update lines 85/88/91 in tests/test_text_onnx_embeddings.py to use the same capital M.

[stephantul]

The potion multilingual was indeed an issue on our end, thanks for flagging it. I've fixed the keys!

[Dylancouzon]

LGTM, @joein ready for your pass & running the CIs