Feature Extraction
MLX
Safetensors
Transformers
sentence-transformers
qwen3
text-generation
sentence-similarity
text-embeddings-inference
4-bit precision
Instructions to use mlx-community/Qwen3-Embedding-8B-4bit-DWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Qwen3-Embedding-8B-4bit-DWQ with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-Embedding-8B-4bit-DWQ mlx-community/Qwen3-Embedding-8B-4bit-DWQ
- Transformers
How to use mlx-community/Qwen3-Embedding-8B-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-8B-4bit-DWQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mlx-community/Qwen3-Embedding-8B-4bit-DWQ") model = AutoModelForCausalLM.from_pretrained("mlx-community/Qwen3-Embedding-8B-4bit-DWQ") - sentence-transformers
How to use mlx-community/Qwen3-Embedding-8B-4bit-DWQ with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mlx-community/Qwen3-Embedding-8B-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] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
mlx-community/Qwen3-Embedding-8B-4bit-DWQ
This model mlx-community/Qwen3-Embedding-8B-4bit-DWQ was converted to MLX format from Qwen/Qwen3-Embedding-8B using mlx-lm version 0.25.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen3-Embedding-8B-4bit-DWQ")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Hardware compatibility
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4-bit