metadata
language: en
license: apache-2.0
tags:
- feature-extraction
- zen
- zenlm
- hanzo
- zen3
- embedding
- retrieval
pipeline_tag: feature-extraction
library_name: transformers
Zen3 Embedding Small
Compact Zen3 embedding model for high-throughput retrieval applications.
Overview
Built on Zen MoDE (Mixture of Distilled Experts) architecture with small parameters and 8K context window.
Developed by Hanzo AI and the Zoo Labs Foundation.
Quick Start
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("zenlm/zen3-embedding-small")
sentences = [
"The weather is lovely today.",
"It's so sunny outside!",
"He drove to the stadium.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# Compute cosine similarities
similarities = model.similarity(embeddings, embeddings)
print(similarities)
API Access
from openai import OpenAI
client = OpenAI(base_url="https://api.hanzo.ai/v1", api_key="your-api-key")
response = client.embeddings.create(model="zen3-embedding-small", input="Your text here")
print(response.data[0].embedding)
Model Details
| Attribute | Value |
|---|---|
| Parameters | small |
| Architecture | Zen MoDE |
| Context | 8K tokens |
| License | Apache 2.0 |
License
Apache 2.0