Model reference · open weights

all-MiniLM-L6-onnx

Embeddings Qdrant Embeddings 1 build Open weights 1.2M dl/mo

all-MiniLM-L6-onnx is an open-weight embedding model from Qdrant.

  • all-MiniLM-L6-onnx is an ONNX port of the sentence-transformers/all-MiniLM-L6-v2 model, developed by Qdrant for text classification and similarity searches.
  • The model supports a context length of 512 tokens and is distributed under the apache-2.0 license.
  • It is designed for generating text embeddings to facilitate sentence-similarity tasks.

Summary of the Qdrant/all-MiniLM-L6-v2-onnx model card, 2026-10-01

What it is

Released byQdrant
Released2024-01-16

From the model card

What Qdrant says about all-MiniLM-L6-onnx

Read the model card

ONNX port of sentence-transformers/all-MiniLM-L6-v2 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="sentence-transformers/all-MiniLM-L6-v2")
embeddings = list(model.embed(documents))

# [
#     array([
#         0.00611658, 0.00068912, -0.0203846, ..., -0.01751488, -0.01174267,
#         0.01463472
#     ],
#           dtype=float32),
#     array([
#         0.00173448, -0.00329958, 0.01557874, ..., -0.01473586, 0.0281806,
#         -0.00448205
#     ],
#           dtype=float32)
# ]

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

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