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 benchmark
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 (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.
