Feature Extraction
GGUF
English
llama.cpp
qwen3
zen
zenlm
hanzo
embedding
quantized
retrieval
conversational
Instructions to use zenlm/zen-embedding-0.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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 new
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
- Pi new
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
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