Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
text-generation
sentence-similarity
text-embeddings-inference
Instructions to use RedHatAI/Qwen3-Embedding-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RedHatAI/Qwen3-Embedding-8B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RedHatAI/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 RedHatAI/Qwen3-Embedding-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RedHatAI/Qwen3-Embedding-8B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RedHatAI/Qwen3-Embedding-8B") model = AutoModelForCausalLM.from_pretrained("RedHatAI/Qwen3-Embedding-8B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee9fac959dadfe1823ae74157ea5d2cba2881e11b3e0e87f42d700ddede27d7b
- Size of remote file:
- 336 MB
- SHA256:
- 36cbc9c60375693629f25743c1e77ebb1724af58e671b2376463193c7fd21ef6
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