Feature Extraction
Transformers
Safetensors
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qwen3
text-generation
zen
zen-embedding
zenlm
hanzo
embedding
text-embeddings-inference
Instructions to use zenlm/zen-embedding-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenlm/zen-embedding-0.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="zenlm/zen-embedding-0.6B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-embedding-0.6B") model = AutoModelForCausalLM.from_pretrained("zenlm/zen-embedding-0.6B") - Notebooks
- Google Colab
- Kaggle
Zen Embedding 0.6B
0.6B-parameter sentence-embedding model for retrieval-augmented generation, semantic search, and dense retrieval. Part of the Zen embedding family.
Hosted via Hanzo gateway
Served at api.hanzo.ai as zen-embedding-0.6b.
Files
Native HuggingFace safetensors weights, loadable directly with transformers:
from transformers import AutoModel, AutoTokenizer
m = AutoModel.from_pretrained("zenlm/zen-embedding-0.6B")
t = AutoTokenizer.from_pretrained("zenlm/zen-embedding-0.6B")
For GGUF / Ollama deployment, see zenlm/zen-embedding-0.6B-GGUF.
Acknowledgements
Built on Qwen/Qwen3-Embedding-0.6B (Apache-2.0). Mirrored here as the canonical Zen embedding family entry.
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