Instructions to use nisavid/mxbai-rerank-large-v2-OptiQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nisavid/mxbai-rerank-large-v2-OptiQ-4bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("nisavid/mxbai-rerank-large-v2-OptiQ-4bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - sentence-transformers
How to use nisavid/mxbai-rerank-large-v2-OptiQ-4bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nisavid/mxbai-rerank-large-v2-OptiQ-4bit") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use nisavid/mxbai-rerank-large-v2-OptiQ-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "nisavid/mxbai-rerank-large-v2-OptiQ-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "nisavid/mxbai-rerank-large-v2-OptiQ-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nisavid/mxbai-rerank-large-v2-OptiQ-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
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Model size
0.3B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit
Model tree for nisavid/mxbai-rerank-large-v2-OptiQ-4bit
Base model
mixedbread-ai/mxbai-rerank-large-v2