Text Ranking
sentence-transformers
Safetensors
MLX
English
qwen3
finance
legal
code
stem
medical
mlx-my-repo
6-bit
Instructions to use lexrivera/zerank-2-reranker-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lexrivera/zerank-2-reranker-mlx-6Bit with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("lexrivera/zerank-2-reranker-mlx-6Bit") 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) - MLX
How to use lexrivera/zerank-2-reranker-mlx-6Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir zerank-2-reranker-mlx-6Bit lexrivera/zerank-2-reranker-mlx-6Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| { | |
| "bos_token_id": 151643, | |
| "do_sample": true, | |
| "eos_token_id": [ | |
| 151645, | |
| 151643 | |
| ], | |
| "pad_token_id": 151643, | |
| "temperature": 0.6, | |
| "top_k": 20, | |
| "top_p": 0.95, | |
| "transformers_version": "4.57.1" | |
| } | |