Instructions to use Itopoly/G9v3-3B-Q4_K_M-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 Itopoly/G9v3-3B-Q4_K_M-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 Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M

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 Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M

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 Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Itopoly/G9v3-3B-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Itopoly/G9v3-3B-Q4_K_M-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": "Itopoly/G9v3-3B-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M

Ollama
How to use Itopoly/G9v3-3B-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Itopoly/G9v3-3B-Q4_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M

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": "Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Itopoly/G9v3-3B-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use Itopoly/G9v3-3B-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.G9v3-3B-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Itopoly/G9v3-3B-Q4_K_M-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 Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M

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 Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Itopoly/G9v3-3B-Q4_K_M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M

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 "Itopoly/G9v3-3B-Q4_K_M-GGUF:Q4_K_M" \
  --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 Q4_K_M GGUF — 6-core CPU tier

A CPU-tier-validated Q4_K_M cut of ai9stars/G9v3-3B (dense ~3B, LlamaForCausalLM, 131K context, think/no-think modes, XML tool calling), packaged for llama.cpp on mid-range machines. The GGUF is quantized by mradermacher/G9v3-3B-GGUF; this repo adds the serving template and measured performance numbers for the 6-core tier. Companion repo: Itopoly/G9v3-3B-Q3_K_M-GGUF (the 4-core tier cut).

What's in this repo

File	Size	What it is
`G9v3-3B.Q4_K_M.gguf`	1.8 GB	the quantized model (4.9 BPW)
`g9v3_chat_template_low.jinja`	12 KB	chat template — required for tool calling (see below)

Measured performance (6 threads, x86-64 AVX2)

Measured on an 8-vCPU EPYC @ 2.0 GHz run at 6 threads, with llama.cpp llama-server:

Decode: ~25 tok/s at short context (~15 tok/s at 4K ctx, ~10 tok/s at 8K ctx — CPU decode degrades ~5 ms/token per 1K of context)
Prefill: ~73 tok/s — a 5.5K-token prompt costs ~75–90 s once; the prefix cache makes repeat requests near-instant
A/B vs Q3_K_M at identical settings: Q3 ≈ 26 tok/s, Q4 ≈ 25 tok/s — on a 3B, quant level barely moves decode; the bandwidth difference is too small. Q4 wins on quality (visibly fewer 3-bit artifacts, coherent reasoning, correct "Paris" sanity answer). Threads matter more than quant on a 3B.
-fa OFF recommended — flash-attention is slower on CPU

RAM need: ≥10 GB free for 32K context (KV cache is 52 KiB/token on this model); 131K context fits a 12 GB box.

Run with llama.cpp

# get the files
huggingface-cli download Itopoly/G9v3-3B-Q4_K_M-GGUF G9v3-3B.Q4_K_M.gguf --local-dir .
huggingface-cli download Itopoly/G9v3-3B-Q4_K_M-GGUF g9v3_chat_template_low.jinja --local-dir .

# serve (OpenAI-compatible on /v1/chat/completions, model name: g9v3-3b-q4_k_m)
llama-server -m G9v3-3B.Q4_K_M.gguf --alias g9v3-3b-q4_k_m \
  -t 6 -tb 8 -c 32768 -fa off --port 8000 --host 0.0.0.0 \
  --chat-template-file g9v3_chat_template_low.jinja

Reasoning text arrives in message.reasoning, the answer in content.

Why the chat template matters

Don't serve this GGUF without g9v3_chat_template_low.jinja: the stock template drops tool-result messages, and the model re-calls the same tool forever (an observed loop bug). The bundled template normalizes legacy tool history so multi-turn tool use works.

Notes

No-think mode: temperature=0.7, top_p=0.95; think mode: temperature=0.9, top_p=0.95.
Prefill is the bottleneck on CPU — keep prompts ≤ 1–2K tokens, or accept a one-time ~75–90 s first hit per distinct large prefix (cached afterwards).
No idle eviction: the model and prompt cache stay resident, so second requests on the same prefix are near-instant.
Tool calling works (verified single-turn + both tool-history formats), but tool-call quality on a 3B is tier-limited — the 39B family models are the tool-heavy choice.

Credits

Original model: ai9stars/G9v3-3B (Apache-2.0)
GGUF quantization: mradermacher/G9v3-3B-GGUF
CPU-tier validation, template, and packaging: Itopoly (see also our Q3_K_M 4-core cut)

Downloads last month: 92

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for Itopoly/G9v3-3B-Q4_K_M-GGUF

Base model

ai9stars/G9v3-3B

Quantized

(14)

this model