Instructions to use jnalv/you-are-a-bug-qwen3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jnalv/you-are-a-bug-qwen3-8B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jnalv/you-are-a-bug-qwen3-8B",
	filename="you-are-a-bug-qwen3-8B-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jnalv/you-are-a-bug-qwen3-8B 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 jnalv/you-are-a-bug-qwen3-8B:Q8_0
# Run inference directly in the terminal:
llama cli -hf jnalv/you-are-a-bug-qwen3-8B:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jnalv/you-are-a-bug-qwen3-8B:Q8_0
# Run inference directly in the terminal:
llama cli -hf jnalv/you-are-a-bug-qwen3-8B:Q8_0

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 jnalv/you-are-a-bug-qwen3-8B:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf jnalv/you-are-a-bug-qwen3-8B:Q8_0

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 jnalv/you-are-a-bug-qwen3-8B:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jnalv/you-are-a-bug-qwen3-8B:Q8_0

Use Docker

docker model run hf.co/jnalv/you-are-a-bug-qwen3-8B:Q8_0

LM Studio
Jan

vLLM

How to use jnalv/you-are-a-bug-qwen3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jnalv/you-are-a-bug-qwen3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jnalv/you-are-a-bug-qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jnalv/you-are-a-bug-qwen3-8B:Q8_0

Ollama
How to use jnalv/you-are-a-bug-qwen3-8B with Ollama:
```
ollama run hf.co/jnalv/you-are-a-bug-qwen3-8B:Q8_0
```

Unsloth Studio

How to use jnalv/you-are-a-bug-qwen3-8B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jnalv/you-are-a-bug-qwen3-8B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jnalv/you-are-a-bug-qwen3-8B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jnalv/you-are-a-bug-qwen3-8B to start chatting

How to use jnalv/you-are-a-bug-qwen3-8B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jnalv/you-are-a-bug-qwen3-8B:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jnalv/you-are-a-bug-qwen3-8B:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jnalv/you-are-a-bug-qwen3-8B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jnalv/you-are-a-bug-qwen3-8B: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 jnalv/you-are-a-bug-qwen3-8B:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use jnalv/you-are-a-bug-qwen3-8B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jnalv/you-are-a-bug-qwen3-8B: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 "jnalv/you-are-a-bug-qwen3-8B: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"

Docker Model Runner
How to use jnalv/you-are-a-bug-qwen3-8B with Docker Model Runner:
```
docker model run hf.co/jnalv/you-are-a-bug-qwen3-8B:Q8_0
```

Lemonade

How to use jnalv/you-are-a-bug-qwen3-8B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jnalv/you-are-a-bug-qwen3-8B:Q8_0

Run and chat with the model

lemonade run user.you-are-a-bug-qwen3-8B-Q8_0

List all available models

lemonade list

You Are a Bug — DM Model (Qwen3-8B)

Fine-tune of Qwen3-8B for the You Are a Bug improvisational dungeon master, built for the Hugging Face Build Small Hackathon "Thousand Token Wood" track.

Status: Active checkpoint. Replaced the earlier Llama-3.2-3B base after playtesting showed the 3B model wasn't holding up.

What it does

Given a structured "card" (PLAYER / THE WORLD / WHAT JUST HAPPENED / PLAYER'S TURN), outputs a single JSON object: either a say (narration) or a roll (stat check with difficulty band). Code owns all randomness, arithmetic, and sheet mutations — the model only narrates, decides if a roll is needed, and proposes the difficulty and which stat is tested.

Intended use

🪲 Drives the Gradio app here! 🪲 Very structured output tailor-made for the game engine — not for general-purpose chatting.

Training

Base: Qwen/Qwen3-8B
Method: SFT to create a LoRA, merged, quantized, and exported as GGUF (Q8_0).
Dataset: a blend of hand-curated and bulk-generated turns covering a wide range of game states, user inputs, and bug types.
Hardware: Modal.com