The crispy sentence embedding family from Mixedbread.

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mixedbread-ai/mxbai-embed-xsmall-v1

This model is an open-source English embedding model developed by Mixedbread. It's built upon sentence-transformers/all-MiniLM-L6-v2 and trained with the AnglE loss and Espresso. Read more details in our blog post.

In a bread loaf:

• State-of-the-art performance
• Supports both binary quantization and Matryoshka Representation Learning (MRL).
• Optimized for retrieval tasks
• 4096 context support

Performance

Binary Quantization and Matryoshka

Our model supports both binary quantization and Matryoshka Representation Learning (MRL), allowing for significant efficiency gains:

• Binary quantization: Retains 93.9% of performance while increasing efficiency by a factor of 32
• MRL: A 33% reduction in vector size still leaves 96.2% of model performance

These optimizations can lead to substantial reductions in infrastructure costs for cloud computing and vector databases. Read more here.

Quickstart

Here are several ways to produce German sentence embeddings using our model.

angle-emb

pip install -U angle-emb

from angle_emb import AnglE
from angle_emb.utils import cosine_similarity

# 1. Specify preferred dimensions
dimensions = 384

# 2. Load model and set pooling strategy to avg
model = AnglE.from_pretrained(
    "mixedbread-ai/mxbai-embed-xsmall-v1",
    pooling_strategy='avg').cuda()

query = 'A man is eating a piece of bread'

docs = [
    query,
    "A man is eating food.",
    "A man is eating pasta.",
    "The girl is carrying a baby.",
    "A man is riding a horse.",
]

# 3. Encode
embeddings = model.encode(docs, embedding_size=dimensions)

for doc, emb in zip(docs[1:], embeddings[1:]):
    print(f'{query} ||| {doc}', cosine_similarity(embeddings[0], emb))

Sentence Transformers

python -m pip install -U sentence-transformers

from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

# 1. Specify preferred dimensions
dimensions = 384

# 2. Load model
model = SentenceTransformer("mixedbread-ai/mxbai-embed-xsmall-v1", truncate_dim=dimensions)

query = 'A man is eating a piece of bread'

docs = [
    query,
    "A man is eating food.",
    "A man is eating pasta.",
    "The girl is carrying a baby.",
    "A man is riding a horse.",
]


# 3. Encode
embeddings = model.encode(docs)

similarities = cos_sim(embeddings[0], embeddings[1:])
print('similarities:', similarities)

transformers

pip install -U transformers

from typing import Dict

import torch
import numpy as np
from transformers import AutoModel, AutoTokenizer
from sentence_transformers.util import cos_sim

def pooling(outputs: torch.Tensor, inputs: Dict) -> np.ndarray:
    outputs = torch.sum(
      outputs * inputs["attention_mask"][:, :, None], dim=1) / torch.sum(inputs["attention_mask"])
    return outputs.detach().cpu().numpy()

# 1. Load model
model_id = 'mixedbread-ai/mxbai-embed-xsmall-v1'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id).cuda()

query = 'A man is eating a piece of bread'

docs = [
    query,
    "A man is eating food.",
    "A man is eating pasta.",
    "The girl is carrying a baby.",
    "A man is riding a horse.",
]

# 2. Encode
inputs = tokenizer(docs, padding=True, return_tensors='pt')
for k, v in inputs.items():
    inputs[k] = v.cuda()
outputs = model(**inputs).last_hidden_state
embeddings = pooling(outputs, inputs)

# 3. Compute similarity scores
similarities = cos_sim(embeddings[0], embeddings[1:])
print('similarities:', similarities)

Batched API

python -m pip install batched

import uvicorn
import batched
from fastapi import FastAPI
from fastapi.responses import ORJSONResponse
from sentence_transformers import SentenceTransformer
from pydantic import BaseModel
 
app = FastAPI()
 
model = SentenceTransformer('mixedbread-ai/mxbai-embed-xsmall-v1')
model.encode = batched.aio.dynamically(model.encode)
 
class EmbeddingsRequest(BaseModel):
    input: str | list[str]
 
@app.post("/embeddings")
async def embeddings(request: EmbeddingsRequest):
    return ORJSONResponse({"embeddings": await model.encode(request.input)})
 
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

Community

Join our discord community to share your feedback and thoughts. We're here to help and always happy to discuss the exciting field of machine learning!

License

Apache 2.0

Citation

@online{xsmall2024mxbai,
  title={Every Byte Matters: Introducing mxbai-embed-xsmall-v1},
  author={Sean Lee and Julius Lipp and Rui Huang and Darius Koenig},
  year={2024},
  url={https://www.mixedbread.ai/blog/mxbai-embed-xsmall-v1},
}