Instructions to use JusteLeo/ZAYA1-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JusteLeo/ZAYA1-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="JusteLeo/ZAYA1-8B-GGUF",
	filename="ZAYA1-8B-BF16.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 JusteLeo/ZAYA1-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf JusteLeo/ZAYA1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf JusteLeo/ZAYA1-8B-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 JusteLeo/ZAYA1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf JusteLeo/ZAYA1-8B-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 JusteLeo/ZAYA1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf JusteLeo/ZAYA1-8B-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 JusteLeo/ZAYA1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf JusteLeo/ZAYA1-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/JusteLeo/ZAYA1-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use JusteLeo/ZAYA1-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JusteLeo/ZAYA1-8B-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": "JusteLeo/ZAYA1-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JusteLeo/ZAYA1-8B-GGUF:Q4_K_M

Ollama
How to use JusteLeo/ZAYA1-8B-GGUF with Ollama:
```
ollama run hf.co/JusteLeo/ZAYA1-8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use JusteLeo/ZAYA1-8B-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 JusteLeo/ZAYA1-8B-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 JusteLeo/ZAYA1-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for JusteLeo/ZAYA1-8B-GGUF to start chatting

Pi new

How to use JusteLeo/ZAYA1-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf JusteLeo/ZAYA1-8B-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": "JusteLeo/ZAYA1-8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use JusteLeo/ZAYA1-8B-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 JusteLeo/ZAYA1-8B-GGUF: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 JusteLeo/ZAYA1-8B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use JusteLeo/ZAYA1-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/JusteLeo/ZAYA1-8B-GGUF:Q4_K_M
```

Lemonade

How to use JusteLeo/ZAYA1-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull JusteLeo/ZAYA1-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ZAYA1-8B-GGUF-Q4_K_M

List all available models

lemonade list

ZAYA1-8B GGUF

This repository contains the GGUF quantized formats of the Zyphra/ZAYA1-8B model.

⚠️ Important Note - Experimental Branch: Currently, running this model requires a specific, experimental branch of llama.cpp associated with PR #23112. Please note that because this implementation is still experimental. There may still be bugs, speed issues, or performance issues.

How to use

To run this model, you need to clone a custom fork of llama.cpp and checkout the Zaya1 branch. Follow the steps below:

1. Clone and Build the Custom `llama.cpp`

# Clone the specific repository
git clone https://github.com/Juste-Leo2/llama.cpp.git
cd llama.cpp

# Checkout the experimental branch
git checkout Zaya1

# Build the project (Example with CUDA enabled)
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release

2. Download the Model

Download your preferred quantized model file (e.g., ZAYA1-8B-Q4_K_M.gguf) from this Hugging Face repository and place it in the root directory of the llama.cpp project you just cloned.

3. Run the Model

Once compiled and the model is downloaded, you can run inference using the following command:

./build/bin/llama-cli -m ZAYA1-8B-Q4_K_M.gguf

License

This model is released under the Apache 2.0 License.

Downloads last month: 2,265

GGUF

Model size

9B params

Architecture

zaya

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for JusteLeo/ZAYA1-8B-GGUF

Base model

Zyphra/ZAYA1-base

Finetuned

Zyphra/ZAYA1-reasoning-base

Finetuned

Zyphra/ZAYA1-8B

Quantized

(14)

this model