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] hf download mlx-community/bge-m3-mlx-4bit --local-dir 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 Settings
- LM Studio
- Atomic Chat
Download tokenizer.json from mlx-community/bge-m3-mlx-4bit: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/mlx-community/bge-m3-mlx-4bit/resolve/main/tokenizer.json
- Command line
-
hf download hf://mlx-community/bge-m3-mlx-4bit/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/mlx-community/bge-m3-mlx-4bit/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 4786592ea7559ad79ac9adb5350927747fb7d0e28b608784f8cf9cd36906c228
- Size of remote file:
- 17.1 MB
- SHA256:
- 5df1f55d60c9705a501ab9a75550728625740741fe4be308dac4806c16b7d51d
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