Instructions to use devopsfortunato/Fonzie0.1-4B-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 devopsfortunato/Fonzie0.1-4B-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 devopsfortunato/Fonzie0.1-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf devopsfortunato/Fonzie0.1-4B-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 devopsfortunato/Fonzie0.1-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf devopsfortunato/Fonzie0.1-4B-GGUF:Q4_K_M

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 devopsfortunato/Fonzie0.1-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf devopsfortunato/Fonzie0.1-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/devopsfortunato/Fonzie0.1-4B-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use devopsfortunato/Fonzie0.1-4B-GGUF with Ollama:
```
ollama run hf.co/devopsfortunato/Fonzie0.1-4B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use devopsfortunato/Fonzie0.1-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devopsfortunato/Fonzie0.1-4B-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": "devopsfortunato/Fonzie0.1-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use devopsfortunato/Fonzie0.1-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/devopsfortunato/Fonzie0.1-4B-GGUF:Q4_K_M
```

Lemonade

How to use devopsfortunato/Fonzie0.1-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull devopsfortunato/Fonzie0.1-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Fonzie0.1-4B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use devopsfortunato/Fonzie0.1-4B-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 devopsfortunato/Fonzie0.1-4B-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 devopsfortunato/Fonzie0.1-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use devopsfortunato/Fonzie0.1-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devopsfortunato/Fonzie0.1-4B-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 "devopsfortunato/Fonzie0.1-4B-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"

Fonzie0.1-4B-GGUF

GGUF files of Fonzie0.1-4B: a Qwen3.5-4B fine-tune that searches when it doesn't know, answers with sources, speaks Italian and English, and looks at pictures. It is trained to work inside agent apps (oh-my-pi, opencode). Evaluation, limits and training details are in the model card of the full model, Fonzie0.1-4B.

A newer version is out: Fonzie0.2-4B-GGUF, with memory per context and a Q4_K_M evaluation.

File	Size	Use
`Fonzie0.1-4B-Q8_0.gguf`	4.6 GB	closest to the bf16; not evaluated
`Fonzie0.1-4B-Q4_K_M.gguf`	2.8 GB	smaller, faster
`mmproj-Fonzie0.1-4B-F16.gguf`	0.7 GB	vision projector, needed for pictures

Made with llama.cpp (convert_hf_to_gguf.py, llama-quantize). The Q4_K_M is quantized from an F16 conversion.

Usage

LM Studio: load the model file together with the mmproj file.

llama.cpp:

llama-server -m Fonzie0.1-4B-Q8_0.gguf --mmproj mmproj-Fonzie0.1-4B-F16.gguf --jinja -c 65536 \
  --temp 1.0 --top-p 0.95 --top-k 20

Give it a web search tool: without tools and a system prompt it falls back to the base model's long thinking.

License: CC BY-NC 4.0 (non-commercial; part of the training data is non-commercial). The base model Qwen3.5-4B is Apache 2.0.

Author: Alfonso Fortunato.

Downloads last month: 118

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

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Model tree for devopsfortunato/Fonzie0.1-4B-GGUF

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Quantized

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