m97j
/

npc_LoRA-fps

@@ -1,136 +0,0 @@
----
-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/
-├── adapter/         # LoRA adapter files
-├── basemodel/       # Optional: base model files if Qwen is unavailable
-├── 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: [[email protected]]
-```