| ---
|
| license: apache-2.0
|
| language:
|
| - en
|
| tags:
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| - unity
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| - unity3d
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| - game-development
|
| - csharp
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| - xr
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| - vr
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| - ar
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| - openxr
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| - code
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| - finetuned
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| - lora
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| - qlora
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| base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
|
| datasets:
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| - vishnuOI/unity-dev-instructions
|
| ---
|
|
|
| # Unity Coder 30B
|
|
|
| A QLoRA fine-tuned adapter for [Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct)
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| specialized for Unity game development in C#.
|
|
|
| ## Training
|
|
|
| - **Base model**: Qwen/Qwen3-Coder-30B-A3B-Instruct (30B MoE, 3B active params)
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| - **Method**: QLoRA (4-bit NF4, r=16, alpha=32, target: q/k/v/o/gate/up/down projections)
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| - **Dataset**: [vishnuOI/unity-dev-instructions](https://huggingface.co/datasets/vishnuOI/unity-dev-instructions)
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| - **Training pairs**: 16,604 Unity instruction pairs
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| - **Sources**: Unity docs (32K pairs scraped), Stack Overflow [unity3d], GitHub Unity C# repos
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| - **Hardware**: 2x NVIDIA A100 80GB PCIe
|
|
|
| ## Capabilities
|
|
|
| - Unity C# scripting (MonoBehaviour, ScriptableObjects, coroutines, events)
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| - XR/VR development (OpenXR, XR Interaction Toolkit, spatial anchors)
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| - Physics, animation, UI Toolkit, NavMesh
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| - URP/HDRP shaders and rendering
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| - DOTS/ECS/Burst/Jobs performance patterns
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| - Editor scripting and tooling
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|
|
| ## Usage
|
|
|
| ```python
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| from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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| from peft import PeftModel
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| import torch
|
|
|
| base_model_id = "Qwen/Qwen3-Coder-30B-A3B-Instruct"
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| adapter_id = "vishnuOI/unity-coder-30b"
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|
|
| bnb_config = BitsAndBytesConfig(
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| load_in_4bit=True,
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| bnb_4bit_quant_type="nf4",
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| bnb_4bit_compute_dtype=torch.bfloat16,
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| )
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|
|
| tokenizer = AutoTokenizer.from_pretrained(base_model_id)
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| model = AutoModelForCausalLM.from_pretrained(
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| base_model_id,
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| quantization_config=bnb_config,
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| device_map="auto",
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| torch_dtype=torch.bfloat16,
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| )
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| model = PeftModel.from_pretrained(model, adapter_id)
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| model.eval()
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|
|
| messages = [
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| {"role": "system", "content": "You are an expert Unity game developer."},
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| {"role": "user", "content": "Write a MonoBehaviour that spawns enemies at random positions."},
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| ]
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| text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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| inputs = tokenizer(text, return_tensors="pt").to(model.device)
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|
|
| with torch.no_grad():
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| out = model.generate(**inputs, max_new_tokens=512, temperature=0.1, do_sample=True)
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| print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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| ```
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|
|
