Zen Embedding

High-quality multilingual text embeddings for semantic search and retrieval.

Overview

Built on Zen MoDE (Mixture of Distilled Experts) architecture with various parameters and 8K context window.

Developed by Hanzo AI and the Zoo Labs Foundation.

Quick Start

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)
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="zen-embedding", input="Your text here")
print(response.data[0].embedding)