Model reference · open weights

clip-ViT-B-32-text

Embeddings Qdrant Embeddings 1 build Open weights 6k dl/mo

clip-ViT-B-32-text is an open-weight embedding model from Qdrant.

  • clip-ViT-B-32-text is an ONNX port of the sentence-transformers CLIP model developed by Qdrant.
  • It is designed for text classification and similarity searches, with a context length of 77 tokens.
  • The model is released under the MIT license.

Summary of the Qdrant/clip-ViT-B-32-text model card, 2026-10-01

What it is

Released byQdrant
Released2024-04-30

From the model card

What Qdrant says about clip-ViT-B-32-text

Read the model card

ONNX port of sentence-transformers/clip-ViT-B-32 for text classification and similarity searches.

Usage

Here's an example of performing inference using the model with FastEmbed.

from fastembed import TextEmbedding

documents = [
    "You should stay, study and sprint.",
    "History can only prepare us to be surprised yet again.",
]

model = TextEmbedding(model_name="Qdrant/clip-ViT-B-32-text")
embeddings = list(model.embed(documents))

# [
#     array([1.57889184e-02, -2.21896712e-02, -1.40235685e-02, -2.36918423e-02, ...],
#           dtype=float32)
# ]

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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