Instructions to use mlx-community/Qwen3-Reranker-0.6B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Qwen3-Reranker-0.6B-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download mlx-community/Qwen3-Reranker-0.6B-4bit --local-dir Qwen3-Reranker-0.6B-4bit
- sentence-transformers
How to use mlx-community/Qwen3-Reranker-0.6B-4bit with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("mlx-community/Qwen3-Reranker-0.6B-4bit") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download tokenizer.json from mlx-community/Qwen3-Reranker-0.6B-4bit: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/mlx-community/Qwen3-Reranker-0.6B-4bit/resolve/main/tokenizer.json
- Command line
-
hf download hf://mlx-community/Qwen3-Reranker-0.6B-4bit/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/mlx-community/Qwen3-Reranker-0.6B-4bit/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- dde3c5a4d468da6fe61f11ffa8f04af2d101f8f4a0fe118c37bd31bb8c3017b6
- Size of remote file:
- 11.4 MB
- SHA256:
- be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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