Instructions to use zooai/coder-1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zooai/coder-1-gguf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zooai/coder-1-gguf") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zooai/coder-1-gguf", dtype="auto") - llama-cpp-python
How to use zooai/coder-1-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="zooai/coder-1-gguf", filename="Q3_K_M-GGUF/Q3_K_M-GGUF-00001-of-00024.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use zooai/coder-1-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf zooai/coder-1-gguf:Q3_K_M # Run inference directly in the terminal: llama-cli -hf zooai/coder-1-gguf:Q3_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf zooai/coder-1-gguf:Q3_K_M # Run inference directly in the terminal: llama-cli -hf zooai/coder-1-gguf:Q3_K_M
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 zooai/coder-1-gguf:Q3_K_M # Run inference directly in the terminal: ./llama-cli -hf zooai/coder-1-gguf:Q3_K_M
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 zooai/coder-1-gguf:Q3_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf zooai/coder-1-gguf:Q3_K_M
Use Docker
docker model run hf.co/zooai/coder-1-gguf:Q3_K_M
- LM Studio
- Jan
- vLLM
How to use zooai/coder-1-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zooai/coder-1-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zooai/coder-1-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/zooai/coder-1-gguf:Q3_K_M
- SGLang
How to use zooai/coder-1-gguf with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "zooai/coder-1-gguf" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zooai/coder-1-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "zooai/coder-1-gguf" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zooai/coder-1-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use zooai/coder-1-gguf with Ollama:
ollama run hf.co/zooai/coder-1-gguf:Q3_K_M
- Unsloth Studio new
How to use zooai/coder-1-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 zooai/coder-1-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 zooai/coder-1-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for zooai/coder-1-gguf to start chatting
- Pi new
How to use zooai/coder-1-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf zooai/coder-1-gguf:Q3_K_M
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": "zooai/coder-1-gguf:Q3_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use zooai/coder-1-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 zooai/coder-1-gguf:Q3_K_M
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 zooai/coder-1-gguf:Q3_K_M
Run Hermes
hermes
- Docker Model Runner
How to use zooai/coder-1-gguf with Docker Model Runner:
docker model run hf.co/zooai/coder-1-gguf:Q3_K_M
- Lemonade
How to use zooai/coder-1-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull zooai/coder-1-gguf:Q3_K_M
Run and chat with the model
lemonade run user.coder-1-gguf-Q3_K_M
List all available models
lemonade list
llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
)Zoo Coder-1 GGUF (Quantized Coding Model)
Overview
Zoo Coder-1 GGUF provides quantized versions of our enterprise-grade coding AI model. These GGUF-formatted models enable efficient deployment across various hardware configurations while maintaining excellent coding capabilities.
Model Details
- Base: Qwen3-Coder with A3B technology
- Format: GGUF quantized
- Context: 32K tokens (extensible to 128K)
- Languages: Python, JavaScript, TypeScript, Go, Rust, Java, C++, and 50+ more
Available Quantizations
| Variant | Size | RAM Required | Use Case |
|---|---|---|---|
| Q2_K | ~2GB | 4GB | Edge devices, prototyping |
| Q3_K_M | ~2.5GB | 5GB | Mobile, lightweight servers |
| Q4_K_M | ~3.2GB | 6GB | Recommended - Best balance |
| Q5_K_M | ~4GB | 7GB | High-quality production |
| Q6_K | ~5GB | 8GB | Maximum quality |
Quick Start
With llama.cpp
./main -m Q4_K_M-GGUF/Q4_K_M-GGUF-00001-of-00032.gguf \
-p "Write a Python function to calculate fibonacci numbers"
With Zoo Desktop
zoo model download coder-1-gguf
About Zoo AI
Zoo Labs Foundation Inc is a 501(c)(3) nonprofit organization pioneering accessible AI infrastructure.
- Website: zoo.ngo
- HuggingFace: huggingface.co/zooai
License
Apache 2.0
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="zooai/coder-1-gguf", filename="", )