Feature Extraction
MLX
Safetensors
sentence-transformers
xlm-roberta
embedding
text-embeddings-inference
retrieval
Instructions to use mlx-community/bge-m3-mlx-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/bge-m3-mlx-fp16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir bge-m3-mlx-fp16 mlx-community/bge-m3-mlx-fp16
- sentence-transformers
How to use mlx-community/bge-m3-mlx-fp16 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mlx-community/bge-m3-mlx-fp16") 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 Settings
- LM Studio
- Xet hash:
- 4786592ea7559ad79ac9adb5350927747fb7d0e28b608784f8cf9cd36906c228
- Size of remote file:
- 17.1 MB
- SHA256:
- 5df1f55d60c9705a501ab9a75550728625740741fe4be308dac4806c16b7d51d
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