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"

chengfp-eng commited on 1 day ago

Commit

0bc930d

verified ·

1 Parent(s): 226d8bb

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

Browse files

Files changed (1) hide show

README.md +5 -7

README.md CHANGED Viewed

@@ -62,15 +62,13 @@ Then start a server; it downloads the model on first run and serves an OpenAI-co
 page at `http://localhost:8080`:
 ```bash
-# Full thinking budget (needs about 15 GB of GPU memory)
-llama-server -hf FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0 --jinja -ngl 99 -c 135168
-# 16 GB Macs: 32K context (longer answers are cut off)
-llama-server -hf FirstPrinciplesLabs/quark_quantized_weight_only-Q8_0 --jinja -ngl 99 -c 32768
 ```
-Use the sampling settings from the [Quark model card](https://huggingface.co/FirstPrinciplesLabs/quark#usage)
-(thinking mode: temperature 1.0, top_p 0.95, top_k 20, presence penalty 1.5).
 ## License

 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