Instructions to use WhiskyAKM/G9v3-3B-NVFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use WhiskyAKM/G9v3-3B-NVFP4-GGUF 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 WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4

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 WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4

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 WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use WhiskyAKM/G9v3-3B-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WhiskyAKM/G9v3-3B-NVFP4-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": "WhiskyAKM/G9v3-3B-NVFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4

Ollama
How to use WhiskyAKM/G9v3-3B-NVFP4-GGUF with Ollama:
```
ollama run hf.co/WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4
```
Unsloth Desktop

How to use WhiskyAKM/G9v3-3B-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4

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": "WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use WhiskyAKM/G9v3-3B-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4
```

Lemonade

How to use WhiskyAKM/G9v3-3B-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.G9v3-3B-NVFP4-GGUF-NVFP4

List all available models

lemonade list

Hermes Agent

How to use WhiskyAKM/G9v3-3B-NVFP4-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4

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 WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use WhiskyAKM/G9v3-3B-NVFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4

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 "WhiskyAKM/G9v3-3B-NVFP4-GGUF:NVFP4" \
  --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"

G9v3-3B NVFP4 GGUF

NVFP4 (4-bit floating-point) quantized version of ai9stars/G9v3-3B, a compact 3B-parameter Llama-architecture language model supporting English and Chinese, with a 128K context window, tool-calling, and a built-in thinking/reasoning mode.

Model Overview

G9v3-3B is a lightweight text-generation model built on the Llama architecture. Despite its small size (~3B parameters), it supports a 131,072-token context window (128K) thanks to a high RoPE θ of 5,000,000. The model uses Grouped-Query Attention (GQA) with 2 key-value heads across 16 attention heads for efficient inference, and includes special tokens for thinking/reasoning (<|thought_begin|> / <|thought_end|>, /think, /no_think) and tool calling (<function>, <|tool_call|>, etc.).

The model uses a ChatML-style conversation format with <|im_start|> / <|im_end|> delimiters.

Model Architecture

Property	Value
Architecture	Llama
Parameters	~3B
Hidden Size	2048
Intermediate Size	6144
Layers	52
Attention Heads	16
KV Heads	2 (GQA)
Head Dimension	128
Context Length	131,072
Vocabulary Size	130,560
RoPE Theta	5,000,000
Original Precision	bfloat16
Supported Languages	en, zh

Quantization

This model is quantized with NVFP4 (NVIDIA 4-bit floating point). NVFP4 is NVIDIA's 4-bit floating-point format that preserves a wider dynamic range than integer 4-bit formats (such as Q4_K), offering better accuracy while remaining highly memory-efficient.

File	Quantization	Size	Precision
`g9v3-3b-nvfp4.gguf`	NVFP4	1.7 GB	4-bit float

Usage

llama.cpp CLI

./llama-cli \
  -m g9v3-3b-nvfp4.gguf \
  -p "Explain quantum computing in simple terms." \
  --temp 0.9 --top-p 0.95

llama-server (OpenAI-compatible API)

./llama-server \
  -m g9v3-3b-nvfp4.gguf \
  --host 0.0.0.0 --port 8080

Thinking Mode

The model supports a thinking/reasoning mode controlled via special tokens. Use /think to enable extended reasoning or /no_think to disable it. When thinking is enabled, the model outputs its reasoning between <|thought_begin|> and <|thought_end|> tokens before providing the final answer.

Tool Calling

The model supports function/tool calling via XML-style <function> tags. Tool definitions are injected into the system prompt, and the model responds with <function name="..."> blocks containing <param> elements.

Generation Parameters

Recommended parameters from the original model's generation_config.json:

Parameter	Value
Temperature	0.9
Top-P	0.95
BOS Token ID	0
EOS Token IDs	1, 130073
Pad Token ID	1

Acknowledgements

Original model: ai9stars/G9v3-3B
GGUF reference: WhiskyAKM/G9v3-3B-GGUF
Format tooling: llama.cpp

License

Apache License 2.0

Downloads last month: 16

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

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