How to use mlx-community/Qwen3-Embedding-4B-4bit-DWQ with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-Embedding-4B-4bit-DWQ mlx-community/Qwen3-Embedding-4B-4bit-DWQ
How to use mlx-community/Qwen3-Embedding-4B-4bit-DWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mlx-community/Qwen3-Embedding-4B-4bit-DWQ")
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mlx-community/Qwen3-Embedding-4B-4bit-DWQ") model = AutoModelForCausalLM.from_pretrained("mlx-community/Qwen3-Embedding-4B-4bit-DWQ")
How to use mlx-community/Qwen3-Embedding-4B-4bit-DWQ with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mlx-community/Qwen3-Embedding-4B-4bit-DWQ") 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]
estefanotuyama