ue-expert-v2 (Qwen2.5-Coder-14B-Instruct + SFT)

Fine-tuned Unreal Engine 5 expert model, specialized in C++ and Blueprint development.

Model Details

• Base model: Qwen2.5-Coder-14B-Instruct
• Fine-tuning: QLoRA (rank 32, alpha 64) SFT on 27,738 curated UE5 Q&A pairs
• Negative examples: 9.3% of training data teaches the model to say "I don't know" for hallucinated/non-existent APIs
• Quantization: Q4_K_M (4.87 bits per weight)
• Size: ~8.4 GB GGUF

Training Data

• 27,738 training pairs covering UE5 C++ APIs, Blueprint patterns, architecture, and best practices
• 7 template categories for positive examples (hierarchy, API lookup, code patterns, etc.)
• Negative examples include fabricated class names, non-existent functions, and plausible-but-wrong API claims
• Quality-gated: all pairs scored >= 0.4 by automated quality pipeline

Usage with Ollama

# Download the GGUF and Modelfile, then:
ollama create ue-expert -f Modelfile
ollama run ue-expert "What is the parent class of ACharacter?"

Files

• model-q4_k_m.gguf — Quantized model (Q4_K_M, 8.4 GB)
• sft_adapter/ — LoRA adapter weights (for further fine-tuning)
• Modelfile — Ollama model definition with ChatML template

Training Metrics

Metric	Value
Steps	1299 (3 epochs)
Final train loss	0.668
Final eval loss	0.677
Hardware	RunPod A100 SXM 80GB
Training time	~3h 46m
VRAM usage	15.8 GB / 80 GB

Part of game-dev-docs

This model is the synthesis layer for a RAG + fine-tuned model + MCP server pipeline that provides deep Unreal Engine awareness to Claude Code. The RAG pipeline provides retrieval over 302K indexed documentation chunks; this model provides internalized domain knowledge for synthesis and judgment calls.