metadata
license: cc-by-4.0
language:
- en
tags:
- unity
- unity3d
- game-development
- csharp
- xr
- vr
- ar
- openxr
- code
- finetuned
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
datasets:
- vishnuOI/unity-dev-instructions
Unity Coder 7B
A fine-tuned version of Qwen2.5-Coder-7B-Instruct specialized for Unity game development in C#.
Training
- Base model: Qwen/Qwen2.5-Coder-7B-Instruct
- Method: QLoRA (4-bit NF4, r=16, alpha=32)
- Dataset: vishnuOI/unity-dev-instructions
- Training pairs: 1,687 Unity C# instruction pairs
- Epochs: 3
Capabilities
- Unity C# scripting (MonoBehaviour, ScriptableObjects, coroutines)
- XR/VR development (OpenXR, XR Interaction Toolkit)
- Physics, animation, UI Toolkit
- Editor scripting and tooling
- Performance optimization patterns
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "vishnuOI/unity-coder-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are an expert Unity game developer specializing in C# scripting and XR development."},
{"role": "user", "content": "How do I detect collision between two objects in Unity?"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))