Feature Extraction
MLX
Safetensors
English
French
xlm-roberta
mlx-embeddings
embeddings
sentence-similarity
bge
apple-silicon
quantized
Instructions to use TyKaoz/bge-m3-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TyKaoz/bge-m3-6bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir bge-m3-6bit TyKaoz/bge-m3-6bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
BGE-M3 — 6-bit MLX
6-bit MLX quantization of
BAAI/bge-m3, for Apple Silicon (~457 MB).
Multilingual text-embedding model (1024-dim dense vectors).
Usage
pip install -U mlx-embeddings
from mlx_embeddings import load, generate
model, tokenizer = load("TyKaoz/bge-m3-6bit")
out = generate(model, tokenizer, texts=["Bonjour", "Hello", "ä½ å¥½"])
print(out.text_embeds.shape) # (3, 1024)
| Base | Tool | Precision | Size |
|---|---|---|---|
BAAI/bge-m3 |
mlx-embeddings |
6-bit · group 64 | ~457 MB |
By TyKaoz — privacy-first native macOS LLM chat client. Apache 2.0, inherited from the base model.
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Model size
0.1B params
Tensor type
F16
·
U32 ·
Hardware compatibility
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Quantized
Model tree for TyKaoz/bge-m3-6bit
Base model
BAAI/bge-m3