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:
- 6aec39639a0a2d1ca966356b8c2b8426a484f80ff80731f44fa8482040713bdf
- Size of remote file:
- 11.4 MB
- SHA256:
- aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
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