Instructions to use mlx-community/Qwen3-Reranker-0.6B-mxfp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/Qwen3-Reranker-0.6B-mxfp8 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mlx-community/Qwen3-Reranker-0.6B-mxfp8") model = AutoModelForCausalLM.from_pretrained("mlx-community/Qwen3-Reranker-0.6B-mxfp8") - MLX
How to use mlx-community/Qwen3-Reranker-0.6B-mxfp8 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-Reranker-0.6B-mxfp8 mlx-community/Qwen3-Reranker-0.6B-mxfp8
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
mlx-community/Qwen3-Reranker-0.6B-mxfp8
The Model mlx-community/Qwen3-Reranker-0.6B-mxfp8 was converted to MLX format from Qwen/Qwen3-Reranker-0.6B using mlx-embeddings version 0.0.3.
Use with mlx
pip install mlx-embeddings
from mlx_embeddings import load, generate
import mlx.core as mx
model, tokenizer = load("mlx-community/Qwen3-Reranker-0.6B-mxfp8")
# For text embeddings
output = generate(model, processor, texts=["I like grapes", "I like fruits"])
embeddings = output.text_embeds # Normalized embeddings
# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)
print("Similarity matrix between texts:")
print(similarity_matrix)
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Qwen/Qwen3-0.6B-Base