- Libraries
- MLX
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
- Transformers
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", device_map="auto") - sentence-transformers
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] - Inference
-
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
