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"

Matthew Tchouikine commited on 6 days ago

Commit

b3cc926

1 Parent(s): 88e3a40

add generation speed and prompt reading speed numbers

Browse files

Files changed (1) hide show

README.md +24 -5

README.md CHANGED Viewed

@@ -44,12 +44,31 @@ All differences are within run-to-run variation: the 8-bit model matches the ori
 ## Speed
-Generation speed for a single user with llama.cpp (tokens per second):
-| Hardware | Empty context | 32K tokens of context |
-|---|---|---|
-| NVIDIA H200 | 216 | 191 |
-| MacBook Pro, Apple M4, 16 GB | 11.2 | 8.7 |
 On Apple Silicon, speed scales with memory bandwidth (about 90% of it is used), so Macs with faster
 memory run proportionally faster. The model needs about 10 GB of memory plus room for the context;

 ## Speed
+Speed for a single user with llama.cpp (tokens per second), by how much context is already filled.
+Generation (writing the answer):
+| Hardware | Context | Q8_0 | bf16 |
+|---|---|---|---|
+| NVIDIA H200 | empty | 216 | 185 |
+| NVIDIA H200 | 32K | 191 | 167 |
+| NVIDIA H200 | 100K | 156 | 139 |
+| MacBook Pro, M4, 16 GB | empty | 11.2 | does not fit (18.4 GB) |
+| MacBook Pro, M4, 16 GB | 32K | 8.7 | does not fit |
+Prompt reading (a 512-token prompt):
+| Hardware | Context | Q8_0 | bf16 |
+|---|---|---|---|
+| NVIDIA H200 | empty | 9,106 | 16,587 |
+| NVIDIA H200 | 32K | 7,677 | 12,200 |
+| NVIDIA H200 | 100K | 5,544 | 7,800 |
+| MacBook Pro, M4, 16 GB | empty | 208 | does not fit |
+| MacBook Pro, M4, 16 GB | 32K | 139 | does not fit |
+Generation is limited by memory speed, where the smaller Q8_0 file is faster. Prompt reading is limited
+by compute, where llama.cpp runs bf16 faster on NVIDIA GPUs. For thinking-mode answers, generation
+takes most of the time.
 On Apple Silicon, speed scales with memory bandwidth (about 90% of it is used), so Macs with faster
 memory run proportionally faster. The model needs about 10 GB of memory plus room for the context;