Feature Extraction
sentence-transformers
Safetensors
Transformers
MLX
qwen3
text-generation
sentence-similarity
text-embeddings-inference
Instructions to use arthurcollet/Qwen3-Embedding-4B-mlx-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use arthurcollet/Qwen3-Embedding-4B-mlx-nvfp4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("arthurcollet/Qwen3-Embedding-4B-mlx-nvfp4") 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] - Transformers
How to use arthurcollet/Qwen3-Embedding-4B-mlx-nvfp4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="arthurcollet/Qwen3-Embedding-4B-mlx-nvfp4")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("arthurcollet/Qwen3-Embedding-4B-mlx-nvfp4") model = AutoModelForCausalLM.from_pretrained("arthurcollet/Qwen3-Embedding-4B-mlx-nvfp4") - MLX
How to use arthurcollet/Qwen3-Embedding-4B-mlx-nvfp4 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-Embedding-4B-mlx-nvfp4 arthurcollet/Qwen3-Embedding-4B-mlx-nvfp4
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
arthurcollet/Qwen3-Embedding-4B-mlx-nvfp4
The Model arthurcollet/Qwen3-Embedding-4B-mlx-nvfp4 was converted to MLX format from Qwen/Qwen3-Embedding-4B using mlx-lm version 0.0.6.
Use with mlx
pip install mlx-embeddings
from mlx_embeddings import load, generate
import mlx.core as mx
model, tokenizer = load("arthurcollet/Qwen3-Embedding-4B-mlx-nvfp4")
# 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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Model size
0.8B params
Tensor type
U8
路
U32 路
F16 路
Hardware compatibility
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Base model
Qwen/Qwen3-4B-Base