Feature Extraction
MLX
Safetensors
qwen2
embeddings
sentence-similarity
quantization
omlx
q8
8-bit precision
custom_code
Instructions to use TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q8 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q8 --local-dir gte-Qwen2-1.5B-instruct-MLX-Q8
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download tokenizer.json from TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q8: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q8/resolve/main/tokenizer.json
- Command line
-
hf download hf://TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q8/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q8/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 7a5b95162b7e9d02537ad3b506c88ee09dae7c886c1f70c8b6afbbf7ce2c456a
- Size of remote file:
- 11.4 MB
- SHA256:
- 48f722bc04c884e2fe1525fdcd85a1293a8499b6e620c1ac7c083c49632305fb
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