Instructions to use samdotci/Qwen3-Reranker-0.6B-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samdotci/Qwen3-Reranker-0.6B-mlx-4Bit with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("samdotci/Qwen3-Reranker-0.6B-mlx-4Bit") model = AutoModelForCausalLM.from_pretrained("samdotci/Qwen3-Reranker-0.6B-mlx-4Bit") - MLX
How to use samdotci/Qwen3-Reranker-0.6B-mlx-4Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-Reranker-0.6B-mlx-4Bit samdotci/Qwen3-Reranker-0.6B-mlx-4Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Xet hash:
- 68d9a35a897ccd8c2500f38090f668b6779148fb10066e8f215cccfa5c44e218
- Size of remote file:
- 335 MB
- SHA256:
- 690c240f8f472279ee762dc4023a33dd87703cab316d8e5559c036bc7fa53f53
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