Instructions to use simpledirect/Vinci-Piccolo-1.0-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 simpledirect/Vinci-Piccolo-1.0-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 simpledirect/Vinci-Piccolo-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf simpledirect/Vinci-Piccolo-1.0-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 simpledirect/Vinci-Piccolo-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf simpledirect/Vinci-Piccolo-1.0-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 simpledirect/Vinci-Piccolo-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf simpledirect/Vinci-Piccolo-1.0-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 simpledirect/Vinci-Piccolo-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf simpledirect/Vinci-Piccolo-1.0-GGUF:Q4_K_M

Use Docker

docker model run hf.co/simpledirect/Vinci-Piccolo-1.0-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use simpledirect/Vinci-Piccolo-1.0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "simpledirect/Vinci-Piccolo-1.0-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": "simpledirect/Vinci-Piccolo-1.0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/simpledirect/Vinci-Piccolo-1.0-GGUF:Q4_K_M

Ollama
How to use simpledirect/Vinci-Piccolo-1.0-GGUF with Ollama:
```
ollama run hf.co/simpledirect/Vinci-Piccolo-1.0-GGUF:Q4_K_M
```
Unsloth Desktop

How to use simpledirect/Vinci-Piccolo-1.0-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf simpledirect/Vinci-Piccolo-1.0-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": "simpledirect/Vinci-Piccolo-1.0-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use simpledirect/Vinci-Piccolo-1.0-GGUF with Docker Model Runner:
```
docker model run hf.co/simpledirect/Vinci-Piccolo-1.0-GGUF:Q4_K_M
```

Lemonade

How to use simpledirect/Vinci-Piccolo-1.0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull simpledirect/Vinci-Piccolo-1.0-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Vinci-Piccolo-1.0-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use simpledirect/Vinci-Piccolo-1.0-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 simpledirect/Vinci-Piccolo-1.0-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 simpledirect/Vinci-Piccolo-1.0-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use simpledirect/Vinci-Piccolo-1.0-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf simpledirect/Vinci-Piccolo-1.0-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 "simpledirect/Vinci-Piccolo-1.0-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"

Vinci Piccolo 1.0 — GGUF

GGUF (quantized) builds of Vinci Piccolo, for local inference with Ollama, LM Studio, and llama.cpp. For the full-precision weights, evals, and details, see simpledirect/Vinci-Piccolo-1.0.

These files are not the measured artifact

🔴 No GGUF file in this repository has been evaluated. The benchmark figures on the full-precision card were measured on the unquantized bf16 weights, not on any file here. Quantization shifts scores, and with no per-tier measurement this card cannot say in which direction or by how much for any tier.

This card therefore publishes no per-tier score, no quality ranking between the tiers, and no recommended tier. The file sizes and memory figures below are measured, and describe storage and memory cost only. A file's presence in this repository is not a performance claim about that tier, and not a claim of parity with the full-precision weights.

Available variants

File	Size	Min RAM	Notes
`vinci-piccolo-1.0-20260629-Q6_K.gguf`	3.46 GB	12 GB	6-bit K-quant
`vinci-piccolo-1.0-20260629-Q5_K_M.gguf`	3.07 GB	10 GB	5-bit K-quant, medium mixture
`vinci-piccolo-1.0-20260629-Q4_K_M.gguf`	2.71 GB	8 GB	4-bit K-quant, medium mixture

GPU: Q5_K_M and Q4_K_M run on 4 GB VRAM; Q6_K needs 6 GB. Mac M-series: Q5_K_M fits on 8 GB unified memory; Q6_K needs 16 GB.

Ollama

ollama run hf.co/simpledirect/Vinci-Piccolo-1.0-GGUF

llama.cpp

./llama-cli \
    -m vinci-piccolo-1.0-20260629-Q5_K_M.gguf \
    --ctx-size 262144 \
    --temp 0 \
    --chat-template qwen3

llama-server (OpenAI-compatible API)

./llama-server \
    -m vinci-piccolo-1.0-20260629-Q5_K_M.gguf \
    --ctx-size 262144 \
    --host 0.0.0.0 \
    --port 8080

Prompt format

Qwen / ChatML chat template. No system prompt required — character is trained into the weights. Pass enable_thinking=False when using the tokenizer directly to suppress <think> output.

Citation

@misc{simpledirect2026vinci,
  title        = {Vinci Piccolo 1.0},
  author       = {{SimpleDirect}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/simpledirect/Vinci-Piccolo-1.0}},
  note         = {Apache 2.0. Fine-tuned from Qwen/Qwen3.5-4B.},
}