Instructions to use zenlm/zen-embedding-0.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zenlm/zen-embedding-0.6B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="zenlm/zen-embedding-0.6B-GGUF",
	filename="zen-embedding-0.6B-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "\"Today is a sunny day and I will get some ice cream.\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use zenlm/zen-embedding-0.6B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf zenlm/zen-embedding-0.6B-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf zenlm/zen-embedding-0.6B-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf zenlm/zen-embedding-0.6B-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf zenlm/zen-embedding-0.6B-GGUF:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf zenlm/zen-embedding-0.6B-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf zenlm/zen-embedding-0.6B-GGUF:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf zenlm/zen-embedding-0.6B-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zenlm/zen-embedding-0.6B-GGUF:Q8_0

Use Docker

docker model run hf.co/zenlm/zen-embedding-0.6B-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use zenlm/zen-embedding-0.6B-GGUF with Ollama:
```
ollama run hf.co/zenlm/zen-embedding-0.6B-GGUF:Q8_0
```

Unsloth Studio

How to use zenlm/zen-embedding-0.6B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for zenlm/zen-embedding-0.6B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for zenlm/zen-embedding-0.6B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for zenlm/zen-embedding-0.6B-GGUF to start chatting

How to use zenlm/zen-embedding-0.6B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf zenlm/zen-embedding-0.6B-GGUF:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "zenlm/zen-embedding-0.6B-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zenlm/zen-embedding-0.6B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf zenlm/zen-embedding-0.6B-GGUF:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default zenlm/zen-embedding-0.6B-GGUF:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use zenlm/zen-embedding-0.6B-GGUF with Docker Model Runner:
```
docker model run hf.co/zenlm/zen-embedding-0.6B-GGUF:Q8_0
```

Lemonade

How to use zenlm/zen-embedding-0.6B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zenlm/zen-embedding-0.6B-GGUF:Q8_0

Run and chat with the model

lemonade run user.zen-embedding-0.6B-GGUF-Q8_0

List all available models

lemonade list

Zen Embedding 0.6b Gguf

GGUF quantized 0.6B Zen Embedding model for efficient semantic search on CPU.

Overview

GGUF quantization for efficient CPU and mixed CPU/GPU inference using llama.cpp and compatible runtimes.

Developed by Hanzo AI and the Zoo Labs Foundation.

Quick Start

# Download and run with llama.cpp
./llama-cli -m zen-embedding-0.6B.Q4_K_M.gguf -p "Hello, how can I help you?" -n 512

# With llama-cpp-python
from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="zenlm/zen-embedding-0.6B-GGUF",
    filename="*Q4_K_M.gguf",
)
output = llm("Hello!", max_tokens=512)
print(output["choices"][0]["text"])

Model Details

Attribute	Value
Parameters	0.6B
Format	GGUF (quantized)
Context	8K tokens
License	Apache 2.0

License

Apache 2.0

Downloads last month: 187

GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

8-bit

Model tree for zenlm/zen-embedding-0.6B-GGUF

Base model

zenlm/zen-embedding

Quantized

(2)

this model

Collection including zenlm/zen-embedding-0.6B-GGUF

Zen Embeddings

Collection

Embedding model family for RAG and semantic search. • 7 items • Updated 3 days ago