File size: 1,380 Bytes
ba22296 57119ef f182c2e 57119ef f182c2e 57119ef f182c2e 57119ef ba22296 f182c2e ba22296 57119ef f182c2e 57119ef ba22296 57119ef ba22296 57119ef f182c2e ba22296 57119ef f182c2e 57119ef f182c2e 57119ef f182c2e 57119ef ba22296 f182c2e 57119ef f182c2e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | ---
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](https://hanzo.ai) and the [Zoo Labs Foundation](https://zoo.ngo).
## Quick Start
```python
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
```python
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
|