Feature Extraction
MLX
Safetensors
sentence-transformers
xlm-roberta
embedding
text-embeddings-inference
retrieval
4bit
quantized
Instructions to use mlx-community/bge-m3-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/bge-m3-mlx-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir bge-m3-mlx-4bit mlx-community/bge-m3-mlx-4bit
- sentence-transformers
How to use mlx-community/bge-m3-mlx-4bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mlx-community/bge-m3-mlx-4bit") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
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