Feature Extraction
Transformers
Safetensors
English
Chinese
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
- Xet hash:
- 7f7828f2f177a8fecc364286be9c2c09ea5c1890f3c6f6b4c0ef6aa5f57d44c5
- Size of remote file:
- 11.4 MB
- SHA256:
- def76fb086971c7867b829c23a26261e38d9d74e02139253b38aeb9df8b4b50a
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