chicksana28
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exaone-lora

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 ---
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 ---
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 ---
+license: apache-2.0
+base_model: LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct
+tags:
+- peft
+- lora
+- korean
+- rag
+- exaone
+language:
+- ko
 ---
+# EXAONE RAG Fine-tuned Model with LoRA
+이 모델은 EXAONE-3.5-2.4B-Instruct를 기반으로 한국어 RAG 데이터셋으로 파인튜닝된 모델입니다.
 ## Model Details
+- **Base Model**: LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct
+- **Fine-tuning Method**: QLoRA (4-bit quantization + LoRA)
+- **Task**: Retrieval-Augmented Generation (RAG)
+- **Language**: Korean
+- **Training Data**: RAFT methodology based Korean RAG dataset
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+# 베이스 모델과 토크나이저 로드
+base_model = AutoModelForCausalLM.from_pretrained("LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct")
+# LoRA 어댑터 적용
+model = PeftModel.from_pretrained(base_model, "ryanu/my-exaone-raft-model")
+# 추론 예시
+messages = [
+    {"role": "system", "content": "주어진 컨텍스트를 바탕으로 질문에 답변하세요."},
+    {"role": "user", "content": "컨텍스트: 한국의 수도는 서울입니다. 질문: 한국의 수도는?""}
+]
+input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer.encode(input_text, return_tensors="pt")
+with torch.no_grad():
+    outputs = model.generate(inputs, max_new_tokens=100, temperature=0.7)
+response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
+print(response)
+```
 ## Training Details
+- **Training Framework**: Hugging Face Transformers + PEFT
+- **Optimization**: 8-bit AdamW
+- **Learning Rate**: 1e-4
+- **Batch Size**: 32 (with gradient accumulation)
+- **Precision**: FP16
+## Performance
+이 모델은 베이스라인 EXAONE 모델 대비 한국어 RAG 태스크에서 향상된 성능을 보입니다.
+자세한 평가 결과는 학습 리포지토리를 참고하세요.