Instructions to use FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0
# Run inference directly in the terminal:
llama cli -hf FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0
# Run inference directly in the terminal:
llama cli -hf FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0

Use Docker

docker model run hf.co/FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0

Ollama
How to use FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0 with Ollama:
```
ollama run hf.co/FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0
```
Unsloth Desktop

How to use FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0 with Docker Model Runner:
```
docker model run hf.co/FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0
```

Lemonade

How to use FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0

Run and chat with the model

lemonade run user.quark_quantized_weight_only-Q8_0-Q8_0

List all available models

lemonade list

Hermes Agent

How to use FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0: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 FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Quark int8

Quark, a 9B reasoning model for physics, science and maths by FirstPrinciples, quantized to int8 for llama.cpp and tools built on it (Ollama, LM Studio). It is also on Ollama as FirstPrinciples/quark-q8.

File	Format	Size	Input
`quark-Q8_0.gguf`	Q8_0 (int8)	9.8 GB	text

Q8_0 is llama.cpp's int8 format: weights are stored as 8-bit integers, with one scale shared by each block of 32.

Results

The int8 version matches the original bf16 model on GPQA Diamond, AIME 2025, MMLU-Pro Physics and Humanity's Last Exam, and generates faster. See the Quark model card for the benchmark scores and throughput on an NVIDIA H200 and a MacBook Pro (M4, 16 GB).

Usage

Ollama

The int8 model is on Ollama as FirstPrinciples/quark-q8, with the recommended sampling settings and a 131,072-token context built in. Install Ollama, then run:

ollama run FirstPrinciples/quark-q8

A 131,072-token context needs about 15 GB of GPU memory. On a 16 GB Mac, lower it inside the chat with /set parameter num_ctx 32768 (longer answers are then cut off)

llama.cpp

Install llama.cpp, which includes llama-server, following its installation guide (Homebrew, winget, conda, Nix, and builds for NVIDIA GPUs).

Then start a server; it downloads the model on first run and serves an OpenAI-compatible API and a chat page at http://localhost:8080:

llama-server -hf FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0 --jinja -c 131072 \
  --temp 1.0 --top-k 20 --top-p 0.95 --presence-penalty 1.5

This sets the recommended sampling settings for thinking mode (from the Quark model card). A 131,072-token context needs about 15 GB of GPU memory; on a 16 GB Mac, use -c 32768 instead (longer answers are then cut off).