Text Ranking
Transformers
Safetensors
sentence-transformers
MLX
qwen2
text-generation
mlx-my-repo
text-embeddings-inference
4-bit precision
Instructions to use nisavid/mxbai-rerank-large-v2-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nisavid/mxbai-rerank-large-v2-mlx-4bit with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nisavid/mxbai-rerank-large-v2-mlx-4bit") model = AutoModelForCausalLM.from_pretrained("nisavid/mxbai-rerank-large-v2-mlx-4bit") - sentence-transformers
How to use nisavid/mxbai-rerank-large-v2-mlx-4bit with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("nisavid/mxbai-rerank-large-v2-mlx-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) - MLX
How to use nisavid/mxbai-rerank-large-v2-mlx-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir mxbai-rerank-large-v2-mlx-4bit nisavid/mxbai-rerank-large-v2-mlx-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
nisavid/mxbai-rerank-large-v2-mlx-4Bit
The Model nisavid/mxbai-rerank-large-v2-mlx-4Bit was converted to MLX format from mixedbread-ai/mxbai-rerank-large-v2 using mlx-lm version 0.31.2.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("nisavid/mxbai-rerank-large-v2-mlx-4Bit")
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
0.2B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit
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Base model
mixedbread-ai/mxbai-rerank-large-v2