yeongseok11
/

hyperclovax-1.5b-erp-nl2sql

@@ -1,199 +1,98 @@
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 ---
+license: cc-by-nc-4.0
+library_name: peft
+base_model: naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B
+tags:
+- text-to-sql
+- erp
+- hyperclova
+- korean
+- nlp
+- lora
+- generated_from_trainer
 ---
+# HyperCLOVAX-1.5B-ERP-SQL 🚀
+이 모델은 [naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B](https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B)를 기반으로 **한국어 ERP 도메인의 Text-to-SQL 작업**을 위해 파인튜닝된 모델입니다.
+1.5B라는 초경량 모델임에도 불구하고, 파인튜닝 후 **0.5%에서 62.0%로 극적인 성능 향상**을 달성했습니다. 특히 복잡한 추론(Lv 5) 영역에서는 2B급 모델들을 상회하는 효율성을 보여줍니다.
+## 📊 모델 성능 (Dramatic Improvement)
+자체 구축한 ERP-SQL 데이터셋 평가 결과입니다. 사전 학습(Baseline) 상태에서는 도메인 지식이 없어 거의 정답을 맞히지 못했으나, 학습 후 실무 투입 가능한 수준으로 환골탈태하였습니다.
+| 모델 (Model) | 학습 상태 | 전체 정확도 | Lv 1 (쉬움) | **Lv 5 (매우 어려움)** |
+| :--- | :--- | :--- | :--- | :--- |
+| **HyperCLOVA X 1.5B** | Baseline | 0.5% | 2.5% | 0.0% |
+| **HyperCLOVA X 1.5B** | **Fine-tuned (Ours)** | **62.0%** | **92.5%** | **47.5%** |
+> **핵심 분석:** 전체 정확도는 2B급 모델 대비 소폭 낮을 수 있으나, **고난이도(Lv 5) 추론 정확도(47.5%)**는 2.1B 경쟁 모델(45.0%)보다 오히려 높게 측정되었습니다. 이는 모델의 파라미터 밀도가 매우 효율적임을 시사합니다.
+## 🔧 학습 정보 (Training Details)
+* **베이스 모델:** naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B
+* **학습 방법:** LoRA (Low-Rank Adaptation)
+* **최적 에폭(Epoch):** 5 (지속적인 성능 우상향 확인)
+* **데이터셋:** 스키마가 반영된 합성(Synthetic) 한국어 ERP 질문-쿼리 쌍
+* **하드웨어:** NVIDIA RTX 4060 Ti (16GB) x 2ea
+## 💻 사용 가이드 (How to Use)
+### 1. 라이브러리 설치
+```bash
+pip install torch transformers peft accelerate
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+# 1. 모델 로드
+base_model_id = "naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B"
+adapter_id = "yeongseok11/hyperclovax-1.5b-erp-nl2sql"
+tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+model = PeftModel.from_pretrained(base_model, adapter_id)
+model.eval()
+# 2. 프롬프트 정의
+schema_context = """
+[Tables]
+employees(emp_id, name, dept_id, hire_date, salary)
+departments(dept_id, dept_name, location)
+"""
+question = "인사팀 직원들의 이름을 알려줘."
+prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+### Instruction:
+아래 스키마를 참고하여 질문을 SQL로 변환하세요.
+### Input:
+### 질문:
+{question}
+### 스키마:
+{schema_context}
+### Response:
+"""
+# 3. 추론
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        do_sample=False,
+        eos_token_id=tokenizer.eos_token_id
+    )
+print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("### Response:")[-1].strip())