Instructions to use doobz1111/idiotbot-v3-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use doobz1111/idiotbot-v3-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="doobz1111/idiotbot-v3-gguf", filename="idiotbot-v3-q4_k_m.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 doobz1111/idiotbot-v3-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 doobz1111/idiotbot-v3-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf doobz1111/idiotbot-v3-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 doobz1111/idiotbot-v3-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf doobz1111/idiotbot-v3-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 doobz1111/idiotbot-v3-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf doobz1111/idiotbot-v3-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 doobz1111/idiotbot-v3-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf doobz1111/idiotbot-v3-gguf:Q4_K_M
Use Docker
docker model run hf.co/doobz1111/idiotbot-v3-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use doobz1111/idiotbot-v3-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "doobz1111/idiotbot-v3-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": "doobz1111/idiotbot-v3-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/doobz1111/idiotbot-v3-gguf:Q4_K_M
- Ollama
How to use doobz1111/idiotbot-v3-gguf with Ollama:
ollama run hf.co/doobz1111/idiotbot-v3-gguf:Q4_K_M
- Unsloth Studio
How to use doobz1111/idiotbot-v3-gguf 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 doobz1111/idiotbot-v3-gguf 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 doobz1111/idiotbot-v3-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for doobz1111/idiotbot-v3-gguf to start chatting
- Pi
How to use doobz1111/idiotbot-v3-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf doobz1111/idiotbot-v3-gguf:Q4_K_M
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": "doobz1111/idiotbot-v3-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use doobz1111/idiotbot-v3-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 doobz1111/idiotbot-v3-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 doobz1111/idiotbot-v3-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use doobz1111/idiotbot-v3-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf doobz1111/idiotbot-v3-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 "doobz1111/idiotbot-v3-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"
- Docker Model Runner
How to use doobz1111/idiotbot-v3-gguf with Docker Model Runner:
docker model run hf.co/doobz1111/idiotbot-v3-gguf:Q4_K_M
- Lemonade
How to use doobz1111/idiotbot-v3-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull doobz1111/idiotbot-v3-gguf:Q4_K_M
Run and chat with the model
lemonade run user.idiotbot-v3-gguf-Q4_K_M
List all available models
lemonade list
IdiotBot v3 (Q4_K_M GGUF)
A fine-tuned Qwen3-8B model trained on Australian IRC channel logs. Captures the casual, irreverent tone of Australian internet culture โ witty, crude, and unapologetically Aussie.
Model Details
| Base Model | Qwen/Qwen3-8B (Instruct) |
| Method | QLoRA (4-bit) via Unsloth |
| LoRA Rank | 16 (alpha 32) |
| Training Data | ~90K curated multi-turn IRC conversations |
| Format | ChatML |
| Sequence Length | 1024 |
| Epochs | 1 |
| Learning Rate | 2e-5 (cosine schedule) |
| Effective Batch Size | 16 (8 x 2 gradient accumulation) |
| Quantization | Q4_K_M (4.7 GB) |
| Hardware | NVIDIA H100 NVL (~2.5 hours) |
Training Results
| Step | Train Loss | Eval Loss |
|---|---|---|
| 2000 | 1.52 | 1.50 |
| 3000 | 1.43 | - |
| 4000 | 1.40 | - |
| 5000 | 1.39 | 1.38 |
Train and eval loss converged closely, indicating good generalisation with no overfitting.
Training Data
Sourced from IRC chat logs spanning multiple years across several Australian channels. Over 1 million raw messages were cleaned, threaded into conversations, and quality-scored, resulting in ~90K high-quality multi-turn training examples.
Data Pipeline
- Parse IRC logs to structured JSONL
- Clean noise, normalise nicks, thread conversations
- Pattern-based annotation (topics, quality scoring, tone)
- Quality-weighted selection (higher quality conversations upweighted)
- Format as ChatML with channel context
Usage with Ollama
ollama create idiotbot -f Modelfile
Example Modelfile:
FROM ./idiotbot-v3-q4_k_m.gguf
TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
{{ .Response }}<|im_end|>"""
PARAMETER temperature 0.7
PARAMETER top_p 0.8
PARAMETER top_k 20
PARAMETER repeat_penalty 1.1
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
Recommended Inference Settings
| Parameter | Value |
|---|---|
| temperature | 0.7 |
| top_p | 0.8 |
| top_k | 20 |
| repeat_penalty | 1.1 |
| think | false |
Set think: false to disable Qwen3 thinking mode.
Limitations
- Trained specifically on Australian IRC chat โ not a general-purpose model
- Contains casual language, slang, and crude humour
- Not suitable for professional or sensitive applications
- Designed as an IRC chatbot persona, not an assistant
License
Apache 2.0 (inherits from Qwen3-8B base model)
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4-bit