Feature Extraction
Transformers
Safetensors
qwen3
retrieval
dense-retrieval
information-retrieval
embedding
agentic-search
deep-research
text-embeddings-inference
Instructions to use Yuqi-Zhou/LRAT-Qwen3-Embedding-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yuqi-Zhou/LRAT-Qwen3-Embedding-0.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Yuqi-Zhou/LRAT-Qwen3-Embedding-0.6B")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Yuqi-Zhou/LRAT-Qwen3-Embedding-0.6B") model = AutoModel.from_pretrained("Yuqi-Zhou/LRAT-Qwen3-Embedding-0.6B") - Notebooks
- Google Colab
- Kaggle
File size: 129 Bytes
8b90198 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:4b4ce50aacda932caf8ddb46290d9b5da5cdc8c2932fab8d799ccec8a58bca24
size 7953
|