Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
text-generation
sentence-similarity
text-embeddings-inference
Instructions to use Qwen/Qwen3-Embedding-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Qwen/Qwen3-Embedding-8B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qwen/Qwen3-Embedding-8B") 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 Qwen/Qwen3-Embedding-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Qwen/Qwen3-Embedding-8B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Embedding-8B") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Embedding-8B", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
MRL Performance loss
#14
by psych0v0yager - opened
What is the expected performance loss for reducing the embedding output dim? What degredation should I expect going from 4096->1024, or 4096->256? Is it better to use the 0.6B model that natively outputs 1024 dims or the 8B model with MRL enabled?