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:
- ed34435f34976d79920fba1e3f36c2ca760eec3a308e2aa4c5d79db5b9b44e80
- Size of remote file:
- 4.98 GB
- SHA256:
- 30b1d4c53d84eb018f642cad7b373f0aabf79699872d8702c1f38577c0a59a2f
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