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README.md

@@ -1,202 +1,130 @@
 ---
 base_model: Qwen/Qwen3-8B
 library_name: peft
+language:
+- ko
+- en
+license: apache-2.0
+tags:
+- korean
+- qwen3
+- lora
+- finetuned
+- deepspeed
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-
-
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-## Uses
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-### Direct Use
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-### Framework versions
-
-- PEFT 0.15.2
+# Qwen3-8B Korean Finetuned Model
+
+이 모델은 Qwen3-8B를 한국어 데이터로 파인튜닝한 LoRA 모델입니다.
+
+## 모델 상세 정보
+
+- **기본 모델**: Qwen/Qwen3-8B
+- **파인튜닝 방법**: LoRA (Low-Rank Adaptation)
+- **훈련 프레임워크**: DeepSpeed ZeRO-2 + Transformers
+- **언어**: 한국어, 영어
+- **개발자**: supermon2018
+
+## 훈련 구성
+
+### LoRA 설정
+- **Rank (r)**: 4
+- **Alpha**: 8
+- **Dropout**: 0.05
+- **Target Modules**: qkv_proj, o_proj, gate_proj, up_proj, down_proj
+
+### 훈련 파라미터
+- **Epochs**: 2
+- **Batch Size**: 2 per device
+- **Gradient Accumulation**: 8 steps
+- **Learning Rate**: 2e-4
+- **Precision**: BF16
+- **Optimizer**: AdamW
+
+### 하드웨어
+- **GPU**: 3x RTX 4090 (24GB each)
+- **분산 훈련**: DeepSpeed ZeRO-2
+- **메모리 최적화**: Gradient Checkpointing
+
+## 사용 방법
+
+### 의존성 설치
+```bash
+pip install torch transformers peft
+```
+
+### 모델 로드 및 사용
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# 기본 모델과 토크나이저 로드
+base_model_name = "Qwen/Qwen3-8B"
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+
+# LoRA 어댑터 로드
+model = PeftModel.from_pretrained(
+    model, 
+    "supermon2018/qwen3-8b-korean-finetuned"
+)
+
+# 추론
+def generate_response(prompt, max_length=512):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_length=max_length,
+            temperature=0.7,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response[len(prompt):].strip()
+
+# 사용 예시
+prompt = "안녕하세요. 한국어로 대화해 주세요."
+response = generate_response(prompt)
+print(response)
+```
+
+## 성능 및 특징
+
+- **메모리 효율성**: LoRA를 사용하여 16MB 크기의 가벼운 어댑터
+- **다국어 지원**: 한국어와 영어 모두 지원
+- **빠른 추론**: 기본 모델에 어댑터만 추가하여 빠른 로딩
+
+## 제한사항
+
+- 이 모델은 LoRA 어댑터이므로 기본 Qwen3-8B 모델과 함께 사용해야 합니다
+- 특정 도메인이나 태스크에 따라 추가 파인튜닝이 필요할 수 있습니다
+
+## 라이선스
+
+Apache 2.0 라이선스를 따릅니다.
+
+## 인용
+
+이 모델을 사용하실 때는 다음과 같이 인용해 주세요:
+
+```bibtex
+@misc{qwen3-korean-finetuned,
+  author = {supermon2018},
+  title = {Qwen3-8B Korean Finetuned Model},
+  year = {2024},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/supermon2018/qwen3-8b-korean-finetuned}
+}
+```
+
+## 문의사항
+
+모델 사용 중 문의사항이 있으시면 이슈를 남겨주세요.