Instructions to use umjunsik1323/Qwen2.5-Coder-1.5B-roblox with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use umjunsik1323/Qwen2.5-Coder-1.5B-roblox with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "umjunsik1323/Qwen2.5-Coder-1.5B-roblox")

Transformers

How to use umjunsik1323/Qwen2.5-Coder-1.5B-roblox with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="umjunsik1323/Qwen2.5-Coder-1.5B-roblox")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("umjunsik1323/Qwen2.5-Coder-1.5B-roblox", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use umjunsik1323/Qwen2.5-Coder-1.5B-roblox with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "umjunsik1323/Qwen2.5-Coder-1.5B-roblox"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umjunsik1323/Qwen2.5-Coder-1.5B-roblox",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/umjunsik1323/Qwen2.5-Coder-1.5B-roblox

SGLang

How to use umjunsik1323/Qwen2.5-Coder-1.5B-roblox with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "umjunsik1323/Qwen2.5-Coder-1.5B-roblox" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umjunsik1323/Qwen2.5-Coder-1.5B-roblox",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "umjunsik1323/Qwen2.5-Coder-1.5B-roblox" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umjunsik1323/Qwen2.5-Coder-1.5B-roblox",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use umjunsik1323/Qwen2.5-Coder-1.5B-roblox with Docker Model Runner:
```
docker model run hf.co/umjunsik1323/Qwen2.5-Coder-1.5B-roblox
```

🎮 Qwen2.5 Coder 1.5B Roblox

A specialized code generation model fine-tuned for Roblox Luau programming

⚡ Run in Google Colab (Recommended)

No setup required Click the badge above and run the chatbot instantly in your browser.

🎯 Pre-configured environment
🔥 GPU-accelerated inference
💬 Interactive chat interface
⏱️ Ready in ~3 minutes

📖 Overview

Qwen2.5 Coder 1.5B Roblox is a parameter-efficient fine-tuned model specifically designed for Roblox Luau development. Built on top of Qwen2.5-Coder-1.5B-Instruct, this model excels at generating, completing, and understanding Luau code patterns commonly used in Roblox game development.

🎯 What Makes This Special?

🎮 Roblox-Native: Trained exclusively on authentic Luau code from the official Roblox corpus
🧠 Context-Aware: Understands Roblox-specific APIs, patterns, and best practices

🏗️ Model Architecture

Component	Details
Base Model	Qwen/Qwen2.5-Coder-1.5B-Instruct
Adapter Type	LoRA (Low-Rank Adaptation)
LoRA Rank	8
LoRA Alpha	32
Target Modules	`q_proj`, `v_proj`
Training Hardware	TPU v5e-8 (Multi-core)

📚 Training Details

Dataset

Source: Roblox/luau_corpus
Filtering: Quality-filtered for code length (20-5000 chars) and Luau keyword presence
Split: 90% train / 10% validation

Training Configuration

{
    "max_length": 1024,
    "batch_size": 4,
    "gradient_accumulation_steps": 32,
    "learning_rate": 3e-5,
    "scheduler": "cosine_annealing",
    "epochs": 1,
    "optimizer": "AdamW"
}

🚀 Quick Start

Installation

pip install transformers peft torch

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-1.5B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "umjunsik1323/Qwen2.5-Coder-1.5B-roblox")

# Generate Luau code
messages = [
    {"role": "system", "content": "You are a Roblox Luau programming expert."},
    {"role": "user", "content": "Create a function to make a part glow"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Advanced: Merge and Export

# Merge LoRA weights into base model
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./qwen-luau-merged")

💡 Features

Supported Tasks

✨ Code Completion: Finish partial Luau scripts intelligently
🔧 Function Generation: Create Roblox-specific functions from descriptions
📝 Code Explanation: Understand and document existing Luau code
🐛 Error Fixing: Suggest corrections for common Luau mistakes
🎯 API Usage: Generate proper Roblox API calls

Example Prompts

-- Completion
"local function teleportPlayer(player, position)"
→ Generates complete teleportation logic

-- Generation
"Create a tween that smoothly moves a part to a new position"
→ Generates TweenService implementation

-- Context-Aware
"Handle player damage with a cooldown system"
→ Generates debounce pattern with Humanoid health management

🎯 Use Cases

Game Development

Quick prototyping of Roblox mechanics
Learning Luau programming patterns
Code review and suggestions

Education

Teaching Roblox development
Demonstrating best practices
Interactive coding assistance

Productivity

Accelerating development workflows
Reducing boilerplate code
Standardizing team coding styles

⚠️ Limitations

Scope: Specialized for Luau only, not general-purpose programming
Context Window: Limited to 1024 tokens
Recency: Training data may not include latest Roblox API updates
Validation: Always test generated code in Roblox Studio

📄 Citation

@misc{youngseong_kim_2025,
    author       = { Youngseong Kim },
    title        = { Qwen2.5-Coder-1.5B-roblox (Revision 63e9452) },
    year         = 2025,
    url          = { https://huggingface.co/umjunsik1323/Qwen2.5-Coder-1.5B-roblox },
    doi          = { 10.57967/hf/7093 },
    publisher    = { Hugging Face }
}

📜 License

This LoRA adapter is released under Apache 2.0 License, maintaining compatibility with the base Qwen2.5-Coder model.

🤝 Acknowledgments

Qwen Team at Alibaba Cloud for the base model
Roblox for providing the Luau corpus dataset
Kaggle for providing the computational resources

Made with ❤️ for the Roblox Developer Community

Downloads last month: 33

Model tree for umjunsik1323/Qwen2.5-Coder-1.5B-roblox

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B-Instruct

Adapter

(107)

this model