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
metadata
license: cc-by-nc-4.0
language:
- en
base_model: zeroentropy/zerank-2-reranker
pipeline_tag: text-ranking
tags:
- finance
- legal
- code
- stem
- medical
- mlx
- mlx-my-repo
library_name: sentence-transformers
model_max_length: 32768
lexrivera/zerank-2-reranker-mlx-6Bit
The Model lexrivera/zerank-2-reranker-mlx-6Bit was converted to MLX format from zeroentropy/zerank-2-reranker using mlx-lm version 0.31.2.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("lexrivera/zerank-2-reranker-mlx-6Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)