mlx-community / bge-m3-mlx-8bit

BGE-M3 MLX (8-bit Quantized)

This is the BAAI/bge-m3 model converted to MLX format with 8-bit quantization for Apple Silicon.

Model Description

BGE-M3 is a versatile embedding model capable of:

• Dense retrieval
• Sparse retrieval
• Multi-vector (ColBERT) retrieval

This 8-bit quantized version offers the best quality among quantized variants.

Model Details

Size Comparison

Usage

With MLX

from mlx_embeddings.utils import load_model, load_tokenizer
import mlx.core as mx

model_path = "mlx-community/bge-m3-mlx-8bit"

# Load model and tokenizer
model = load_model(model_path)
tokenizer = load_tokenizer(model_path)

# Generate embeddings
text = "Hello, world!"
tokens = tokenizer.encode(text)
input_ids = mx.array([tokens])
output = model(input_ids)
embedding = output.last_hidden_state.mean(axis=1)  # Mean pooling

print(f"Embedding shape: {embedding.shape}")  # (1, 1024)

With oMLX

curl http://127.0.0.1:8000/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"model": "bge-m3-mlx-8bit", "input": "Your text here"}'

Quantization Details

• Method : Affine quantization
• Bits per weight : 8
• Group size : 64
• Source : Converted from MLX FP16 version

Recommended Use Cases

• Quality-critical applications
• Production deployments where quality is prioritized
• Best choice when memory allows ~600MB

License

MIT license (inherited from BAAI/bge-m3)

Citation

@article{bge_m3,
  title={BGE M3-Embedding: Accurate, Efficient and Versatile Text Embedding},
  author={Chen, Jianlv and Xiao, Shitao and Zhang, Peitian and Luo, Kun and Zhang, Zheng},
  journal={arXiv preprint arXiv:2402.03216},
  year={2024}
}

Disclaimer

This is an unofficial MLX conversion of the BAAI/bge-m3 model. For the original model, see BAAI/bge-m3 .