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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ZygAI/ZygAI_OSS_Flash_GGUF",
	filename="zygai_flash-f16.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_OSS_Flash_GGUF 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_OSS_Flash_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ZygAI/ZygAI_OSS_Flash_GGUF: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_OSS_Flash_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ZygAI/ZygAI_OSS_Flash_GGUF: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_OSS_Flash_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ZygAI/ZygAI_OSS_Flash_GGUF: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_OSS_Flash_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZygAI/ZygAI_OSS_Flash_GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use ZygAI/ZygAI_OSS_Flash_GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZygAI/ZygAI_OSS_Flash_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": "ZygAI/ZygAI_OSS_Flash_GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio new

How to use ZygAI/ZygAI_OSS_Flash_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 ZygAI/ZygAI_OSS_Flash_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 ZygAI/ZygAI_OSS_Flash_GGUF 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_OSS_Flash_GGUF to start chatting

Pi new

How to use ZygAI/ZygAI_OSS_Flash_GGUF 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_OSS_Flash_GGUF: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_OSS_Flash_GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use ZygAI/ZygAI_OSS_Flash_GGUF with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.ZygAI_OSS_Flash_GGUF-Q4_K_M

List all available models

lemonade list

ZygAI OSS Flash GGUF

This repository is the GGUF distribution of ZygAI OSS Flash, released as a legacy milestone by ZygAI’s creator at age 23.

The goal is simple: make the model easy to run locally, easy to share, and easy to preserve.

Legacy Note

ZygAI OSS Flash GGUF is part of the same legacy release as ZygAI/ZygAI_OSS_Flash:

open-source by intention
local-first friendly
shared so the work can outlive its original timeline

If this model helps your project, teaching, or experiments, that is exactly what this release was meant for.

Available GGUF files

zygai_flash-f16.gguf
zygai_flash-q2_k.gguf
zygai_flash-q3_k_s.gguf
zygai_flash-q3_k_m.gguf
zygai_flash-q3_k_l.gguf
zygai_flash-q4_0.gguf
zygai_flash-q4_k_s.gguf
zygai_flash-q4_k_m.gguf
zygai_flash-q5_0.gguf
zygai_flash-q5_k_s.gguf
zygai_flash-q5_k_m.gguf
zygai_flash-q6_k.gguf
zygai_flash-q8_0.gguf

Recommended quant

Start with:

q4_k_m (best default quality/size/speed balance)

Personal Thanks

A huge, heartfelt thank you to Ruby2001, 0daysophie, italian_tech_person, and Julia's Tech Spot.

Your help was not just technical, it was life-changing.
Without you, my life would have been dark.
Thank you for standing with me and helping make ZygAI OSS Flash real.

llama.cpp run example

./llama.cpp/build/bin/llama-cli \
  -m zygai_flash-q4_k_m.gguf \
  -p "Labas! Papasakok trumpą įkvepiančią mintį." \
  -n 256

Downloads last month: -

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for ZygAI/ZygAI_OSS_Flash_GGUF

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Adapter

ZygAI/ZygAI_OSS_Flash

Quantized

(1)

this model