Feature Extraction
MLX
Safetensors
qwen3
embeddings
sentence-similarity
quantization
omlx
q4
4-bit precision
Instructions to use TiGa-RCE/Qwen3-Embedding-8B-MLX-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TiGa-RCE/Qwen3-Embedding-8B-MLX-Q4 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-Embedding-8B-MLX-Q4 TiGa-RCE/Qwen3-Embedding-8B-MLX-Q4
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- 1cc0ea2ab1bbf3cbace4de2db48b896d87952770454248e054846db5aef23597
- Size of remote file:
- 11.4 MB
- SHA256:
- 31f82e8f8c173bff1b34626d065393d33938f6d27a7448cfb706bbf9d3c0e651
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.