Instructions to use ZygAI/zygai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZygAI/zygai with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ZygAI/zygai",
	filename="ZygAI-fp16-00001-of-00004.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 ZygAI/zygai with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ZygAI/zygai:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ZygAI/zygai:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ZygAI/zygai:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ZygAI/zygai:Q4_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 ZygAI/zygai:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ZygAI/zygai:Q4_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 ZygAI/zygai:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZygAI/zygai:Q4_K_M

Use Docker

docker model run hf.co/ZygAI/zygai:Q4_K_M

LM Studio
Jan

vLLM

How to use ZygAI/zygai with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZygAI/zygai"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZygAI/zygai",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZygAI/zygai:Q4_K_M

Ollama
How to use ZygAI/zygai with Ollama:
```
ollama run hf.co/ZygAI/zygai:Q4_K_M
```

Unsloth Studio new

How to use ZygAI/zygai 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 ZygAI/zygai 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 ZygAI/zygai to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ZygAI/zygai to start chatting

Pi new

How to use ZygAI/zygai with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ZygAI/zygai:Q4_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": "ZygAI/zygai:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ZygAI/zygai with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ZygAI/zygai:Q4_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 ZygAI/zygai:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use ZygAI/zygai with Docker Model Runner:
```
docker model run hf.co/ZygAI/zygai:Q4_K_M
```

Lemonade

How to use ZygAI/zygai with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ZygAI/zygai:Q4_K_M

Run and chat with the model

lemonade run user.zygai-Q4_K_M

List all available models

lemonade list

🧠 ZygAI — Neutral Bilingual AI Engine (LT / EN)

ZygAI is a neutral, locally hosted AI engine designed for Lithuanian 🇱🇹 and English 🇬🇧 language tasks.
It is built for local inference, server-based usage, and runtime personas.

ZygAI is a base engine, not a chatbot persona.
Behavior and specialization are applied at runtime (OpenWebUI / API).

✨ Key Features

🇱🇹 / 🇬🇧 True bilingual support
⚡ Optimized GGUF models for llama.cpp
🧩 Supports runtime personas (MiniGPTs, system prompts)
🧠 Clean identity — no vendor branding
🖥️ Designed for systemd + server deployments
🔀 Supports GGUF shards (no merge required)

🧠 Architecture Overview

ZygAI (base engine)
├── Q4 → fast / high throughput
├── Q5 → balanced / general usage
└── Q8 → high quality / reasoning

ZygAI = neutral engine
No hardcoded system prompt in the model

📦 Available Quantizations

Quantization	Purpose	Notes
Q4_K_M	Fast	Best speed, low memory
Q5_K_M	Balanced	Default general use
Q8_0	High quality	Best reasoning, higher RAM

Models may be provided as GGUF shards (-00001-of-00002.gguf).
llama.cpp loads shards automatically — no merge required.

🚀 Running ZygAI (llama.cpp server)

Example: Q4 (shard-based)

./llama-server \
  -m ZygAI-q4_k_m-00001-of-00002.gguf \
  --host 0.0.0.0 \
  --port 8081 \
  --ctx-size 4096 \
  --threads 4 \
  --batch-size 2048 \
  --jinja

Multiple models (recommended)

Model	Port
Q4	8081
Q5	8082
Q8	8083

🧰 Using with OpenWebUI

Provider: OpenAI (local)
Base URL: http://127.0.0.1:PORT/v1
Auth: none

Important

ZygAI is designed for llama.cpp backend.
System prompts and personas work correctly only with llama.cpp, not Ollama.

🎭 Personas (Recommended)

ZygAI is intentionally neutral.
Specialization is applied via runtime personas:

🌍 Language Behavior

Responds in the same language as the user
No automatic language switching
No mixed-language replies unless requested

Examples:
User (EN):
> What is Lithuania?
Assistant:
> Lithuania is a country located in the Baltic region of Eastern Europe.
User (LT):
> Kada Lietuva įstojo į Europos Sąjungą?.
Assistant:
> Lietuva įstojo į Europos Sąjungą 2004 m. gegužės 1 d.

📜 License

Apache 2.0
This repository provides inference-only model files.
Base model weights originate from publicly available sources and are redistributed according to their respective licenses.

🔒 Notes

ZygAI is not ChatGPT
ZygAI is not a vendor-branded assistant
ZygAI is designed for local-first, privacy-respecting AI

📖 Citation

If you use ZygAI in research, development, or documentation, please cite it as follows:

@software{zygai-7b,
  title        = {ZygAI: Neutral Bilingual AI Engine for Lithuanian and English},
  author       = {Mažeika, Žygimantas},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/ZygAI},
  license      = {Apache-2.0},
  note         = {Local-first GGUF models optimized for llama.cpp with runtime personas}
}

Downloads last month: 23

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit

View +1 variant