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:
- 693ec4b3922b0bd306bf7b4989e115ffbfeb7b0c08b31bc6d956818c6bb07f61
- Size of remote file:
- 11.4 MB
- SHA256:
- aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
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