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README.md
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- 작성일: 2026-03-24
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- 상태: active
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---
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## 1. 모델 설명
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### 아키텍처 / 파라미터
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|------|------|
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| 베이스 모델 | Gemma-3-12B-IT |
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| 파라미터 수 | 12B |
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| 학습 방식 | Full Fine-Tuning (SFT) |
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| 출력 구조 | feedback → highlight → decision |
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###
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###
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- **
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##
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- 범용 루브릭 기반 LLM 출력 품질 평가 (Ko Feedback Bench에서 검증된 rubric following 능력)
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- 평가 파이프라인 자동화 시 frontier 모델 대비 비용 효율적 대안으로 활용
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### 학습 코드 스니펫
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```python
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from trl import SFTTrainer, SFTConfig
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from transformers import GemmaForCausalLM, AutoTokenizer
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model_name = "google/gemma-3-12b-it"
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model = GemmaForCausalLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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sft_config = SFTConfig(
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output_dir="./eval-estar-base-v0.1",
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per_device_train_batch_size=1,
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gradient_accumulation_steps=8,
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learning_rate=1e-5,
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num_train_epochs=5,
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eval_strategy="epoch",
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save_strategy="epoch",
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load_best_model_at_end=True, # early stopping 기준: validation loss
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metric_for_best_model="eval_loss",
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greater_is_better=False,
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bf16=True,
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logging_steps=10,
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)
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trainer = SFTTrainer(
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model=model,
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tokenizer=tokenizer,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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args=sft_config,
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)
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trainer.train()
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```
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> ※ 실제 학습 시 validation loss 기준 2 에폭에서 early stopping 적용
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### 추론 코드 스니펫
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_path = "datumo/E-Star-12B-v2-Base"
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model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype="bfloat16", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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# ── System Prompt ──
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system_prompt = """You are a rubric evaluator.
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Your task is to evaluate a response strictly and only according to the provided pass criteria and scoring rubric.
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In your output, return the final evaluation (the three output tags: <feedback>, <highlight>, and <decision>).
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# Evaluation Procedure (must follow all steps):
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1. First, carefully read the Data to Evaluate, the pass criteria, and the scoring rubric to fully understand the requirements.
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2. Evaluate the response only against the given criteria: do not introduce external standards, do not reward style unless the rubric explicitly allows it, and judge by absolute rubric definitions rather than relative comparisons.
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3. Re-check fine-grained details in the response and the rubric, ensuring any tags (if present) are correctly mapped to the pass criteria and that small deviations are not overlooked.
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4. Write criterion-focused feedback that explicitly references the rubric, quoting exact words or phrases from the response when they are decisive, and clearly stating which criteria are satisfied and which are violated.
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5. Finally, extract the key verbatim spans that most influenced your judgment and assign the final score according to the scoring rubric.
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"""
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# ── User Prompt 예시 (Reasoning / Problem Solving) ──
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user_prompt = """You MUST write ALL output (<feedback>, <highlight>, <decision>) in the SAME language as the input question and response being evaluated. If the input is in Korean, your entire output MUST be in Korean.
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# Output Format:
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<feedback>
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Write detailed feedback (reasons) that strictly evaluates the quality of the response using only the given scoring rubric. Do not explicitly state the score in a sentence (e.g., "Therefore, the score is …").
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</feedback>
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<highlight>
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List of words or phrases that you believe are the most important in determining the score.
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</highlight>
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<decision>
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Provide the final integer score assigned based on the scoring rubric.
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</decision>
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# Data to Evaluate
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### Problem
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한 공장에서 하루에 120개의 제품을 생산한다. 불량률이 5%일 때, 일주일(7일) 동안 생산되는 정상 제품의 수는?
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### Model Response
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하루 생산량: 120개
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불량률: 5% → 불량품: 120 × 0.05 = 6개
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하루 정상 제품: 120 - 6 = 114개
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일주일 정상 제품: 114 × 7 = 798개
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### Optional Ground Truth
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798개
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# Rubric
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Evaluate whether the model correctly solves the problem and provides reasoning that is logically consistent with the final answer. Prioritize correctness of the conclusion, then soundness of the reasoning.
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Score 1: The final answer is wrong and the reasoning is invalid, irrelevant, or missing.
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Score 2: The response shows limited progress but contains major reasoning flaws leading to an incorrect or unreliable answer.
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Score 3: The response demonstrates partial reasoning ability but is incomplete, contains mistakes, or reaches an uncertain result.
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Score 4: The response is mostly correct with generally sound reasoning, though minor errors or gaps may remain.
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Score 5: The response reaches the correct answer through clear, consistent, and logically valid reasoning appropriate to the problem."""
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# ── Inference ──
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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]
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input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
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outputs = model.generate(
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input_ids,
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max_new_tokens=2048,
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temperature=0.0,
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do_sample=False,
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)
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response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
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print(response)
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```
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##
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|------|------|
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| 시드 데이터 | K2-Feedback (HAERAEHUB, 2024) — 99.7K |
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| 최종 학습 데이터 | 6,311개 (3단계 필터링 후) |
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|------|-----------|------|
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| Stage 1 | 99.7K → 26K | Qwen3-30B-A3B / Qwen3-Next-80B-A3B 간 초기 합의 |
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| Stage 2 | 26K → 8K | Gemma 베이스 모델 기준 일치/불일치 균형화 |
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| Stage 3 | 8K → 6K | GPT-5.2 단일 평가 + 소형 frontier debate 교차 검증 |
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##
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- 🤗 [datumo/Rag-Quality-Bench](https://huggingface.co/datasets/datumo/Rag-Quality-Bench) — Domain adaptation (금융·법률)
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##
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# SFT Config
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learning_rate: 1e-5
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num_train_epochs: 5 (early stopping at epoch 2)
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per_device_train_batch_size: 1
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gradient_accumulation_steps: 8
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bf16: true
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eval_strategy: epoch
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metric_for_best_model: eval_loss
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load_best_model_at_end: true
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#
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method: Full Fine-Tuning (no LoRA)
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framework: TRL SFTTrainer (von Werra et al., 2020)
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```
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|------|------|
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| GPU | OOO |
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| 학습 시간 | OOO |
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##
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### 5.1 Feedback Bench (영어, Rubric Following)
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| Type | Models | Pearson | Kendall τ | Spearman |
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| Frontier | GPT-5.2 | 0.916 | 0.865 | 0.911 |
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| Frontier | Sonnet-4.6 | 0.840 | 0.776 | 0.847 |
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| Instruct SLM | Gemma-3-12B-IT | 0.810 | 0.725 | 0.794 |
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| Instruct SLM | oss-20b | 0.844 | 0.762 | 0.839 |
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| Evaluator LM | Prometheus-8x7B-v2.0 | 0.823 | 0.736 | 0.806 |
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| Evaluator LM | GLIDER 3.8B | 0.678 | 0.595 | 0.688 |
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| **Ours** | **E-Star-12B-Base** | **0.856** | **0.778** | **0.847** |
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### 5.2 Ko Feedback Bench (한국어, Rubric Following)
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| Type | Models | Pearson | Kendall τ | Spearman |
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| Frontier | GPT-5.2 | 0.929 | 0.886 | 0.925 |
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| Frontier | Sonnet-4.6 | 0.820 | 0.758 | 0.833 |
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| Instruct SLM | Gemma-3-12B-IT | 0.653 | 0.593 | 0.661 |
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| Instruct SLM | oss-20b | 0.778 | 0.704 | 0.779 |
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| Evaluator LM | Prometheus-8x7B-v2.0 | 0.377 | 0.441 | 0.501 |
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| Evaluator LM | GLIDER 3.8B | 0.523 | 0.487 | 0.563 |
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| **Ours** | **E-Star-12B-Base** | **0.826** | **0.754** | **0.819** |
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### 5.3 RAG Quality Bench (금융·법률, Domain Adaptation)
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| Models | LAW(CR) | LAW(FF) | LAW(RR) | FIN(CR) | FIN(FF) | FIN(RR) | Average |
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|--------|---------|---------|---------|---------|---------|---------|---------|
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| GPT-5.2 | 0.846 | 0.785 | 0.941 | 0.882 | 0.740 | 0.970 | 0.861 |
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| Sonnet-4.6 | 0.910 | 0.786 | 0.872 | 0.932 | 0.845 | 0.925 | 0.878 |
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| Gemma-3-12B-IT | 0.620 | 0.742 | 0.742 | 0.830 | 0.713 | 0.821 | 0.745 |
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| oss-20b | 0.846 | 0.722 | 0.870 | 0.793 | 0.752 | 0.900 | 0.813 |
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| Prometheus-8x7B-v2.0 | 0.392 | 0.477 | 0.772 | 0.386 | 0.240 | 0.806 | 0.512 |
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| GLIDER 3.8B | 0.657 | 0.670 | 0.680 | 0.432 | 0.415 | 0.548 | 0.567 |
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| **E-Star-12B-Base** | **0.853** | **0.730** | **0.816** | **0.835** | **0.720** | **0.880** | **0.806** |
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> CR = Context Relevancy, FF = Faithfulness, RR = Response Relevancy
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---
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- 학습 데이터와 벤치마크 레이블이 동일한 debate 기반 절차로 구축되었으므로, 절대적 평가 품질보다는 합의 기반 레이블링 기준과의 정렬 정도를 반영할 수 있음 (도메인 전문가 human evaluation 미실시)
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- Reference-free 설정으로 학습 및 평가되었으므로, reference 포함 환경에서의 성능은 별도 검증 필요
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- 12B 규모 SLM 특성상 frontier 모델 대비 복잡한 루브릭 해석 능력에 한계가 있을 수 있음
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- RAG 평가 시 입력 문서 수 증가에 따른 성능 변화는 미검증
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##
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- 학습 데이터: K2-Feedback (HAERAEHUB, 2024) 라이선스 정책에 따름
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---
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library_name: transformers
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tags: []
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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| 14 |
+
### Model Description
|
| 15 |
|
| 16 |
+
<!-- Provide a longer summary of what this model is. -->
|
| 17 |
|
| 18 |
+
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
|
| 19 |
|
| 20 |
+
- **Developed by:** [More Information Needed]
|
| 21 |
+
- **Funded by [optional]:** [More Information Needed]
|
| 22 |
+
- **Shared by [optional]:** [More Information Needed]
|
| 23 |
+
- **Model type:** [More Information Needed]
|
| 24 |
+
- **Language(s) (NLP):** [More Information Needed]
|
| 25 |
+
- **License:** [More Information Needed]
|
| 26 |
+
- **Finetuned from model [optional]:** [More Information Needed]
|
| 27 |
|
| 28 |
+
### Model Sources [optional]
|
| 29 |
|
| 30 |
+
<!-- Provide the basic links for the model. -->
|
| 31 |
|
| 32 |
+
- **Repository:** [More Information Needed]
|
| 33 |
+
- **Paper [optional]:** [More Information Needed]
|
| 34 |
+
- **Demo [optional]:** [More Information Needed]
|
| 35 |
|
| 36 |
+
## Uses
|
| 37 |
|
| 38 |
+
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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|
| 39 |
|
| 40 |
+
### Direct Use
|
| 41 |
|
| 42 |
+
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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|
| 43 |
|
| 44 |
+
[More Information Needed]
|
| 45 |
|
| 46 |
+
### Downstream Use [optional]
|
| 47 |
|
| 48 |
+
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
| 49 |
|
| 50 |
+
[More Information Needed]
|
| 51 |
|
| 52 |
+
### Out-of-Scope Use
|
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|
| 53 |
|
| 54 |
+
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
| 55 |
|
| 56 |
+
[More Information Needed]
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|
| 57 |
|
| 58 |
+
## Bias, Risks, and Limitations
|
| 59 |
|
| 60 |
+
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
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|
| 61 |
|
| 62 |
+
[More Information Needed]
|
| 63 |
|
| 64 |
+
### Recommendations
|
| 65 |
|
| 66 |
+
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
| 67 |
|
| 68 |
+
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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|
| 69 |
|
| 70 |
+
## How to Get Started with the Model
|
|
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|
| 71 |
|
| 72 |
+
Use the code below to get started with the model.
|
| 73 |
|
| 74 |
+
[More Information Needed]
|
|
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|
| 75 |
|
| 76 |
+
## Training Details
|
| 77 |
|
| 78 |
+
### Training Data
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|
| 79 |
|
| 80 |
+
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
| 81 |
|
| 82 |
+
[More Information Needed]
|
| 83 |
|
| 84 |
+
### Training Procedure
|
|
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|
| 85 |
|
| 86 |
+
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
| 87 |
+
|
| 88 |
+
#### Preprocessing [optional]
|
| 89 |
+
|
| 90 |
+
[More Information Needed]
|
| 91 |
+
|
| 92 |
+
|
| 93 |
+
#### Training Hyperparameters
|
| 94 |
+
|
| 95 |
+
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
| 96 |
+
|
| 97 |
+
#### Speeds, Sizes, Times [optional]
|
| 98 |
+
|
| 99 |
+
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
| 100 |
+
|
| 101 |
+
[More Information Needed]
|
| 102 |
+
|
| 103 |
+
## Evaluation
|
| 104 |
+
|
| 105 |
+
<!-- This section describes the evaluation protocols and provides the results. -->
|
| 106 |
+
|
| 107 |
+
### Testing Data, Factors & Metrics
|
| 108 |
+
|
| 109 |
+
#### Testing Data
|
| 110 |
+
|
| 111 |
+
<!-- This should link to a Dataset Card if possible. -->
|
| 112 |
+
|
| 113 |
+
[More Information Needed]
|
| 114 |
+
|
| 115 |
+
#### Factors
|
| 116 |
+
|
| 117 |
+
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
| 118 |
+
|
| 119 |
+
[More Information Needed]
|
| 120 |
+
|
| 121 |
+
#### Metrics
|
| 122 |
+
|
| 123 |
+
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
| 124 |
+
|
| 125 |
+
[More Information Needed]
|
| 126 |
+
|
| 127 |
+
### Results
|
| 128 |
+
|
| 129 |
+
[More Information Needed]
|
| 130 |
+
|
| 131 |
+
#### Summary
|
| 132 |
+
|
| 133 |
+
|
| 134 |
+
|
| 135 |
+
## Model Examination [optional]
|
| 136 |
+
|
| 137 |
+
<!-- Relevant interpretability work for the model goes here -->
|
| 138 |
+
|
| 139 |
+
[More Information Needed]
|
| 140 |
+
|
| 141 |
+
## Environmental Impact
|
| 142 |
+
|
| 143 |
+
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
| 144 |
+
|
| 145 |
+
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).
|
| 146 |
+
|
| 147 |
+
- **Hardware Type:** [More Information Needed]
|
| 148 |
+
- **Hours used:** [More Information Needed]
|
| 149 |
+
- **Cloud Provider:** [More Information Needed]
|
| 150 |
+
- **Compute Region:** [More Information Needed]
|
| 151 |
+
- **Carbon Emitted:** [More Information Needed]
|
| 152 |
+
|
| 153 |
+
## Technical Specifications [optional]
|
| 154 |
+
|
| 155 |
+
### Model Architecture and Objective
|
| 156 |
+
|
| 157 |
+
[More Information Needed]
|
| 158 |
+
|
| 159 |
+
### Compute Infrastructure
|
| 160 |
+
|
| 161 |
+
[More Information Needed]
|
| 162 |
+
|
| 163 |
+
#### Hardware
|
| 164 |
+
|
| 165 |
+
[More Information Needed]
|
| 166 |
+
|
| 167 |
+
#### Software
|
| 168 |
+
|
| 169 |
+
[More Information Needed]
|
| 170 |
+
|
| 171 |
+
## Citation [optional]
|
| 172 |
+
|
| 173 |
+
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
| 174 |
+
|
| 175 |
+
**BibTeX:**
|
| 176 |
+
|
| 177 |
+
[More Information Needed]
|
| 178 |
+
|
| 179 |
+
**APA:**
|
| 180 |
+
|
| 181 |
+
[More Information Needed]
|
| 182 |
+
|
| 183 |
+
## Glossary [optional]
|
| 184 |
+
|
| 185 |
+
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
| 186 |
+
|
| 187 |
+
[More Information Needed]
|
| 188 |
+
|
| 189 |
+
## More Information [optional]
|
| 190 |
+
|
| 191 |
+
[More Information Needed]
|
| 192 |
+
|
| 193 |
+
## Model Card Authors [optional]
|
| 194 |
+
|
| 195 |
+
[More Information Needed]
|
| 196 |
|
| 197 |
+
## Model Card Contact
|
| 198 |
|
| 199 |
+
[More Information Needed]
|
|
|
config.json
ADDED
|
@@ -0,0 +1,87 @@
|
|
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|
| 1 |
+
{
|
| 2 |
+
"_sliding_window_pattern": 6,
|
| 3 |
+
"architectures": [
|
| 4 |
+
"Gemma3ForCausalLM"
|
| 5 |
+
],
|
| 6 |
+
"attention_bias": false,
|
| 7 |
+
"attention_dropout": 0.0,
|
| 8 |
+
"attn_logit_softcapping": null,
|
| 9 |
+
"bos_token_id": 2,
|
| 10 |
+
"dtype": "float32",
|
| 11 |
+
"eos_token_id": 1,
|
| 12 |
+
"final_logit_softcapping": null,
|
| 13 |
+
"head_dim": 256,
|
| 14 |
+
"hidden_activation": "gelu_pytorch_tanh",
|
| 15 |
+
"hidden_size": 3840,
|
| 16 |
+
"initializer_range": 0.02,
|
| 17 |
+
"intermediate_size": 15360,
|
| 18 |
+
"layer_types": [
|
| 19 |
+
"sliding_attention",
|
| 20 |
+
"sliding_attention",
|
| 21 |
+
"sliding_attention",
|
| 22 |
+
"sliding_attention",
|
| 23 |
+
"sliding_attention",
|
| 24 |
+
"full_attention",
|
| 25 |
+
"sliding_attention",
|
| 26 |
+
"sliding_attention",
|
| 27 |
+
"sliding_attention",
|
| 28 |
+
"sliding_attention",
|
| 29 |
+
"sliding_attention",
|
| 30 |
+
"full_attention",
|
| 31 |
+
"sliding_attention",
|
| 32 |
+
"sliding_attention",
|
| 33 |
+
"sliding_attention",
|
| 34 |
+
"sliding_attention",
|
| 35 |
+
"sliding_attention",
|
| 36 |
+
"full_attention",
|
| 37 |
+
"sliding_attention",
|
| 38 |
+
"sliding_attention",
|
| 39 |
+
"sliding_attention",
|
| 40 |
+
"sliding_attention",
|
| 41 |
+
"sliding_attention",
|
| 42 |
+
"full_attention",
|
| 43 |
+
"sliding_attention",
|
| 44 |
+
"sliding_attention",
|
| 45 |
+
"sliding_attention",
|
| 46 |
+
"sliding_attention",
|
| 47 |
+
"sliding_attention",
|
| 48 |
+
"full_attention",
|
| 49 |
+
"sliding_attention",
|
| 50 |
+
"sliding_attention",
|
| 51 |
+
"sliding_attention",
|
| 52 |
+
"sliding_attention",
|
| 53 |
+
"sliding_attention",
|
| 54 |
+
"full_attention",
|
| 55 |
+
"sliding_attention",
|
| 56 |
+
"sliding_attention",
|
| 57 |
+
"sliding_attention",
|
| 58 |
+
"sliding_attention",
|
| 59 |
+
"sliding_attention",
|
| 60 |
+
"full_attention",
|
| 61 |
+
"sliding_attention",
|
| 62 |
+
"sliding_attention",
|
| 63 |
+
"sliding_attention",
|
| 64 |
+
"sliding_attention",
|
| 65 |
+
"sliding_attention",
|
| 66 |
+
"full_attention"
|
| 67 |
+
],
|
| 68 |
+
"max_position_embeddings": 131072,
|
| 69 |
+
"model_type": "gemma3_text",
|
| 70 |
+
"num_attention_heads": 16,
|
| 71 |
+
"num_hidden_layers": 48,
|
| 72 |
+
"num_key_value_heads": 8,
|
| 73 |
+
"pad_token_id": 0,
|
| 74 |
+
"query_pre_attn_scalar": 256,
|
| 75 |
+
"rms_norm_eps": 1e-06,
|
| 76 |
+
"rope_local_base_freq": 10000.0,
|
| 77 |
+
"rope_scaling": {
|
| 78 |
+
"factor": 8.0,
|
| 79 |
+
"rope_type": "linear"
|
| 80 |
+
},
|
| 81 |
+
"rope_theta": 1000000.0,
|
| 82 |
+
"sliding_window": 1024,
|
| 83 |
+
"transformers_version": "4.57.6",
|
| 84 |
+
"use_bidirectional_attention": false,
|
| 85 |
+
"use_cache": true,
|
| 86 |
+
"vocab_size": 262208
|
| 87 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
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|
|
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|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token_id": 2,
|
| 3 |
+
"cache_implementation": "hybrid",
|
| 4 |
+
"do_sample": true,
|
| 5 |
+
"eos_token_id": [
|
| 6 |
+
1,
|
| 7 |
+
1,
|
| 8 |
+
106
|
| 9 |
+
],
|
| 10 |
+
"pad_token_id": 0,
|
| 11 |
+
"top_k": 64,
|
| 12 |
+
"top_p": 0.95,
|
| 13 |
+
"transformers_version": "4.57.6"
|
| 14 |
+
}
|
model-00001-of-00010.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1b9fc7b75bb0a9e50db2868561053d55a567a967a7028335f15e284321b4ff3b
|
| 3 |
+
size 4987027160
|
model-00002-of-00010.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
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