Instructions to use plated6913/Qwen3-Reranker-8B-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use plated6913/Qwen3-Reranker-8B-mlx-4Bit with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("plated6913/Qwen3-Reranker-8B-mlx-4Bit") model = AutoModelForCausalLM.from_pretrained("plated6913/Qwen3-Reranker-8B-mlx-4Bit") - MLX
How to use plated6913/Qwen3-Reranker-8B-mlx-4Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-Reranker-8B-mlx-4Bit plated6913/Qwen3-Reranker-8B-mlx-4Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Xet hash:
- e809b34ab19f7f0cb3c0806d30ea2f8df829402285da26f5b7f14e1336cfcba6
- Size of remote file:
- 4.61 GB
- SHA256:
- 99ca0934439e3fcfb8a9e767e7af834b056b21a0815b1e0f56cca0cd6ecea5a6
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