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_quantized_weight_only-Q8_0 / README.md

chengfp-eng

Use the same llama-server command as the main Quark card

0bc930d verified 1 day ago

preview code

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2.52 kB

---
license: apache-2.0
base_model:
- FirstPrinciplesLabs/quark
base_model_relation: quantized
pipeline_tag: text-generation
language:
- en
tags:
- gguf
- llama.cpp
- physics
- science
- reasoning
- math
---
# Quark int8

[Quark](https://huggingface.co/FirstPrinciplesLabs/quark), a 9B reasoning model for physics, science and
maths by [FirstPrinciples](https://firstprinciples.com/), quantized to int8 for
[llama.cpp](https://github.com/ggml-org/llama.cpp) and tools built on it (Ollama, LM Studio). It is also on
Ollama as [FirstPrinciples/quark-q8](https://ollama.com/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](https://huggingface.co/FirstPrinciplesLabs/quark#results) and
[throughput](https://huggingface.co/FirstPrinciplesLabs/quark#throughput) on an NVIDIA H200 and a
MacBook Pro (M4, 16 GB).

## Usage

### Ollama

The int8 model is on Ollama as [FirstPrinciples/quark-q8](https://ollama.com/FirstPrinciples/quark-q8),
with the recommended sampling settings and a 131,072-token context built in. Install
[Ollama](https://ollama.com/download), then run:

```bash
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](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) (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`:

```bash
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](https://huggingface.co/FirstPrinciplesLabs/quark#usage)). 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).

## License

Apache 2.0

	---
	license: apache-2.0
	base_model:
	- FirstPrinciplesLabs/quark
	base_model_relation: quantized
	pipeline_tag: text-generation
	language:
	- en
	tags:
	- gguf
	- llama.cpp
	- physics
	- science
	- reasoning
	- math
	---

	# Quark int8

	[Quark](https://huggingface.co/FirstPrinciplesLabs/quark), a 9B reasoning model for physics, science and
	maths by [FirstPrinciples](https://firstprinciples.com/), quantized to int8 for
	[llama.cpp](https://github.com/ggml-org/llama.cpp) and tools built on it (Ollama, LM Studio). It is also on
	Ollama as [FirstPrinciples/quark-q8](https://ollama.com/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](https://huggingface.co/FirstPrinciplesLabs/quark#results) and
	[throughput](https://huggingface.co/FirstPrinciplesLabs/quark#throughput) on an NVIDIA H200 and a
	MacBook Pro (M4, 16 GB).

	## Usage

	### Ollama

	The int8 model is on Ollama as [FirstPrinciples/quark-q8](https://ollama.com/FirstPrinciples/quark-q8),
	with the recommended sampling settings and a 131,072-token context built in. Install
	[Ollama](https://ollama.com/download), then run:

	```bash
	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](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) (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`:

	```bash
	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](https://huggingface.co/FirstPrinciplesLabs/quark#usage)). 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).

	## License

	Apache 2.0