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)
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Model size
0.9B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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6-bit
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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)