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

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

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

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:

Pure Luau Logic & Types (25 questions)
Spatial Math & Vectors (20 questions)
Data Structures & Systems (25 questions)
Defensive Logic & Data (25 questions)

Below are the scores for each section:

Section	qode1:7b	qwen2.5-coder	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.

Downloads last month: -

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

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Model tree for qodelabs/qode1

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Quantized

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this model

qodelabs
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qode1

Model Description

Benchmarks

Uses

Direct Use

Out-of-Scope Use

Bias, Risks, and Limitations

Model tree for qodelabs/qode1

Dataset used to train qodelabs/qode1