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README.md

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