Feature Extraction
sentence-transformers
Safetensors
English
Chinese
qwen3
zen
zen-embedding
zenlm
hanzo
embedding
retrieval
text-embeddings-inference
Instructions to use zenlm/zen-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use zenlm/zen-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("zenlm/zen-embedding") 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] - Notebooks
- Google Colab
- Kaggle
Restore upstream attribution in NOTICE (Qwen3-Embedding-8B)
#2
by zeekay - opened
The shipped NOTICE reads "Copyright 2025-2026 Zen Authors" and names no upstream, but these weights are a derivative of Qwen/Qwen3-Embedding-8B (Apache-2.0).
Apache-2.0 section 4(c) requires retaining upstream attribution notices in distributed derivative works; section 4(d) requires carrying upstream's NOTICE text where upstream ships one. Replacing upstream's copyright with Zen's drops both.
Evidence of derivation: model card declares base_model: Qwen/Qwen3-Embedding-8B
This PR names the upstream and its true license, and keeps Zen's copyright scoped to Zen's own modifications. One LICENSE + one NOTICE; no license change.