Feature Extraction
Transformers
PyTorch
Safetensors
English
bert
zen
zenlm
hanzo
zen3
embedding
retrieval
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen3-embedding-medium")
model = AutoModel.from_pretrained("zenlm/zen3-embedding-medium")Quick Links
Zen3 Embedding Medium
Medium-sized Zen3 embedding model balancing speed and retrieval accuracy.
Overview
Built on Zen MoDE (Mixture of Distilled Experts) architecture with medium 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-medium")
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-medium", input="Your text here")
print(response.data[0].embedding)
Model Details
| Attribute | Value |
|---|---|
| Parameters | medium |
| Architecture | Zen MoDE |
| Context | 8K tokens |
| License | Apache 2.0 |
License
Apache 2.0
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="zenlm/zen3-embedding-medium")