Instructions to use qodelabs/qode1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use qodelabs/qode1 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 qodelabs/qode1:Q4_K_M # Run inference directly in the terminal: llama cli -hf qodelabs/qode1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf qodelabs/qode1:Q4_K_M # Run inference directly in the terminal: llama cli -hf qodelabs/qode1: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 qodelabs/qode1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf qodelabs/qode1: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 qodelabs/qode1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf qodelabs/qode1:Q4_K_M
Use Docker
docker model run hf.co/qodelabs/qode1:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use qodelabs/qode1 with Ollama:
ollama run hf.co/qodelabs/qode1:Q4_K_M
- Unsloth Studio
How to use qodelabs/qode1 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 qodelabs/qode1 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 qodelabs/qode1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for qodelabs/qode1 to start chatting
- Pi
How to use qodelabs/qode1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf qodelabs/qode1: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": "qodelabs/qode1:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use qodelabs/qode1 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf qodelabs/qode1: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 "qodelabs/qode1: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 qodelabs/qode1 with Docker Model Runner:
docker model run hf.co/qodelabs/qode1:Q4_K_M
- Lemonade
How to use qodelabs/qode1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull qodelabs/qode1:Q4_K_M
Run and chat with the model
lemonade run user.qode1-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use qodelabs/qode1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf qodelabs/qode1: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 qodelabs/qode1:Q4_K_M
Run Hermes
hermes
- Atomic Chat
| license: apache-2.0 | |
| base_model: Qwen/Qwen2.5-Coder-7B-Instruct | |
| datasets: | |
| - Pinkstack/Roblox_Luau_CoT_conversational_sharegpt_lqv1 | |
| language: | |
| - en | |
| tags: | |
| - luau | |
| - roblox | |
| - code | |
| - gguf | |
| - qwen2.5 | |
|  | |
| qode1 is a specialised code generation model fine-tuned from `Qwen/Qwen2.5-Coder-7B-Instruct`. It is specifically optimised to generate valid, high-quality **Luau (Roblox Lua)** code using Chain-of-Thought reasoning. | |
| ## Model Description | |
| - **Developed by:** qodelabs | |
| - **Shared by:** qodelabs | |
| - **Model type:** Coder / Fine-tuned Large Language Model | |
| - **Language(s) (NLP):** English | |
| - **License:** Apache 2.0 | |
| - **Finetuned from model:** Qwen/Qwen2.5-Coder-7B-Instruct | |
| # Benchmarks | |
|  | |
| qode-luau-95 is an automated test suite benchmark containing 95 coding challenges designed for conversational instruct models. It is designed specifically to evaluate how accurately an AI model writes Luau code. | |
| Rather than testing general knowledge, qode-luau-95 executes generated code inside a test runner to measure how well the model handles actual Luau scripting requirements. | |
| qode-luau-95 is broken up into 4 unseen sections: | |
| 1. Pure Luau Logic & Types (25 questions) | |
| 2. Spatial Math & Vectors (20 questions) | |
| 3. Data Structures & Systems (25 questions) | |
| 4. Defensive Logic & Data (25 questions) | |
| Below are the scores for each section (all models are their respective instruct variants): | |
| | Section | qode1:7b | qwen2.5-coder:7b | codegemma:7b | llama3:8b | Average | | |
| | :--- | :---: | :---: | :---: | :---: | :---: | | |
| | 1 — Pure Luau Logic & Types | **10/25 (40.0%)** | 9/25 (36.0%) | 9/25 (36.0%) | 9/25 (36.0%) | 37.0% | | |
| | 2 — Spatial Math & Vectors | **8/20 (40.0%)** | **8/20 (40.0%)** | 5/20 (25.0%) | 6/20 (30.0%) | 33.8% | | |
| | 3 — Data Structures & Systems | 5/25 (20.0%) | 5/25 (20.0%) | **7/25 (28.0%)** | 3/25 (12.0%) | 20.0% | | |
| | 4 — Defensive Logic & Data | **11/25 (44.0%)** | 9/25 (36.0%) | 9/25 (36.0%) | 4/25 (16.0%) | 33.0% | | |
| ## Uses | |
| ### Direct Use | |
| qode1 is intended for developers building games, scripts, and systems within the Roblox ecosystem using Luau. It can assist with: | |
| - Writing object-oriented Luau modules and classes. | |
| - Implementing vector math, CFrame operations, and spatial transformations. | |
| - Building game logic, data structures, and state management systems. | |
| ### Out-of-Scope Use | |
| - General-purpose non-coding tasks (e.g., creative writing, general Q&A). | |
| - Generating code for languages outside of Luau (though base capabilities for Python, C++, etc., may partially remain, the model is specialized for Luau). | |
| ## Bias, Risks, and Limitations | |
| - **Syntax Bleed:** The model may occasionally output C-style operators (e.g., ternary `? :` or logical `&&`) due to base model pre-training. Using an explicit system prompt is recommended to strictly enforce Luau syntax rules. | |
| - **Nil Returns in Constructors:** When generating complex OOP structures, always verify that constructors explicitly return `self` or object instances. |