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--- |
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license: mit |
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base_model: Qwen/Qwen2.5-3B-Instruct |
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library_name: peft |
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pipeline_tag: text-generation |
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tags: |
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- lora |
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- transformers |
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- korean |
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- npc |
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- game-ai |
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--- |
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# npc_LoRA |
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**npc_LoRA** is a LoRA adapter built on top of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct), designed to generate emotionally rich, context-aware dialogue for non-player characters (NPCs) in Korean-language game environments. |
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This project is part of a portfolio for industrial service roles in AI and game development, showcasing practical model design, multi-head training, and real-world integration strategies. |
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## 🧠 Model Architecture |
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- **Base model**: Qwen2.5-3B-Instruct |
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- **Adapter type**: LoRA (via PEFT) |
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- **Language**: Korean |
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- **Task**: Text generation with auxiliary heads |
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- **Heads added**: |
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- `delta_head`: Predicts 2D continuous values for narrative state change |
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- `flag_head`: Predicts 3 or more binary flags for game logic triggers |
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## 🏗️ Training Setup |
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- **Environment**: Google Colab with A100 GPU |
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- **Quantization**: 4-bit (nf4) via BitsAndBytes |
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- **Batch size**: 2 (gradient accumulation: 8) |
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- **Epochs**: 6 |
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- **Losses**: |
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- Language modeling (CrossEntropy) |
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- Delta prediction (MSE) |
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- Flag prediction (BCE) |
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## 📜 Prompt Format |
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```text |
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<SYS> |
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NPC_ID=... |
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TAGS: |
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location=... |
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quest_stage=... |
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relationship=... |
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trust=... |
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npc_mood=... |
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player_reputation=... |
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style=... |
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REQUIRE: |
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... |
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FORMAT: |
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<RESPONSE>...</RESPONSE> |
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<DELTA ...> |
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<FLAG ...> |
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</SYS> |
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<CTX> |
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player: ... |
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npc: ... |
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</CTX> |
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<PLAYER>... |
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<NPC> |
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``` |
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## 🔍 Inference Example |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from peft import PeftModel |
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import torch.nn as nn |
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BASE_MODEL = "Qwen/Qwen2.5-3B-Instruct" |
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ADAPTER_PATH = "minjae/npc_LoRA" |
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tokenizer = AutoTokenizer.from_pretrained(ADAPTER_PATH, use_fast=True) |
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model = AutoModelForCausalLM.from_pretrained(BASE_MODEL, device_map="auto", trust_remote_code=True) |
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model = PeftModel.from_pretrained(model, ADAPTER_PATH) |
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# Add heads |
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hidden_size = model.config.hidden_size |
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model.delta_head = nn.Linear(hidden_size, 2).to(model.device) |
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model.flag_head = nn.Linear(hidden_size, 3).to(model.device) |
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prompt = "<SYS>...<CTX>...<PLAYER>...<NPC>" |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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with torch.no_grad(): |
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outputs = model(**inputs, output_hidden_states=True) |
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gen_ids = model.generate(**inputs, max_new_tokens=100) |
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generated_text = tokenizer.decode(gen_ids[0], skip_special_tokens=True) |
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last_hidden = outputs.hidden_states[-1][:, -1, :] |
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delta = model.delta_head(last_hidden) |
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flag = model.flag_head(last_hidden) |
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print("Response:", generated_text) |
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print("Delta:", delta) |
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print("Flags:", torch.sigmoid(flag)) |
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``` |
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## 🧩 Use Cases |
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- NPC dialogue generation in Korean RPGs |
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- Emotionally adaptive storytelling |
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- Game logic trigger prediction (e.g., quest progression, item handoff) |
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## 📁 Repository Structure |
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``` |
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npc_LoRA/ |
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├── lora-output-jason-mom-head/ # LoRA adapter files |
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├── README.md |
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``` |
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## 📌 Notes |
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- Adapter is optimized for Korean-language prompts and multi-turn dialogue. |
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- Designed to integrate with game engines or AI-driven simulation platforms. |
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- Compatible with Hugging Face Spaces (CPU/GPU) and local inference. |
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## 📜 License |
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MIT |
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## 👤 Author |
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Created by **Minjae** |
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Portfolio: [GitHub Profile](https://github.com/m97j) |
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Contact: [[email protected]] |
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