How to use from the
Use from the
Transformers library
# 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")
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mlx-community/Qwen3-Embedding-4B-4bit-DWQ

This model mlx-community/Qwen3-Embedding-4B-4bit-DWQ was converted to MLX format from Qwen/Qwen3-Embedding-4B using mlx-lm version 0.24.0.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Qwen3-Embedding-4B-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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