README.md

---
license: apache-2.0
base_model: Qwen/Qwen3.5-2B
tags:
- qwen
- qwen3.5
- reasoning
- distillation
- claude-opus
- darwin-v8
- sft
- lora
- merged
language:
- en
- ko
- zh
- ja
pipeline_tag: text-generation
library_name: transformers
---

# 🧠 lastbrain — Darwin V8

**Darwin V8 기반 Claude Opus 증류 모델 (2B 파라미터)**

- 👨 **Father (Base)**: [`Qwen/Qwen3.5-2B`](https://huggingface.co/Qwen/Qwen3.5-2B)
- 👩 **Mother (LoRA Adapter)**: [`FINAL-Bench/Qwen3.5-2B-Opus-Distill-v1`](https://huggingface.co/FINAL-Bench/Qwen3.5-2B-Opus-Distill-v1)
- 👶 **Child (This model)**: `FINAL-Bench/lastbrain` — merged full-weight standalone

---

## 📦 특징

- **Base**: Qwen3.5-2B (2.3B 파라미터, 하이브리드 어텐션)
- **Training**: SFT + LoRA (`all-linear`, rank=16, α=32)
- **Teachers**: Claude Opus 4.5 / 4.6, Claude Sonnet 4.6 (pre-generated reasoning traces)
- **Data**: 4,451 고품질 추론 궤적 (4개 공개 데이터셋)
- **Merged**: LoRA 어댑터가 base 가중치에 완전 통합되어 **독립 실행 가능**

---

## 🚀 빠른 사용법

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "FINAL-Bench/lastbrain"
tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

messages = [
    {"role": "user", "content": "If a train travels 60 km in 45 minutes, what is its speed in km/h?"}
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=800,
        do_sample=False,
        pad_token_id=tok.eos_token_id,
    )
print(tok.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
```

**예시 출력**:
```
To find the speed of the train in km/h, we need to convert the given time from minutes to hours.

**Given:**
- Distance = 60 km
- Time = 45 minutes

**Step 1: Convert time to hours**
Since there are 60 minutes in 1 hour:
$$\text{Time in hours} = \frac{45}{60} = 0.75 \text{ hours}$$

**Step 2: Calculate speed**
$$\text{Speed} = \frac{60}{0.75} = 80 \text{ km/h}$$

**Final Answer:** The speed of the train is **80 km/h**.
```

---

## 🧬 Darwin V8 학습 파이프라인

```
[Qwen/Qwen3.5-2B] ──── Base 모델 (동결)
        +
[4,451 Claude Opus/Sonnet reasoning traces]
        ↓
[SFT Training]
  - LoRA (all-linear, r=16, α=32)
  - Learning rate: 2e-4 (V8 rule: ×10 FullFT)
  - 2 epochs, bf16, 8×B200 DDP
  - Loss: 1.33 → 1.10 (-17%)
  - Token accuracy: 68% → 72% (+4%p)
        ↓
[LoRA merge into base weights]
        ↓
[lastbrain] ← 이 모델
```

---

## 📊 학습 데이터 구성

| 데이터셋 | 샘플 수 | 출처 Teacher |
|---------|--------|------|
| [nohurry/Opus-4.6-Reasoning-3000x-filtered](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered) | 2,326 | Claude Opus 4.6 |
| [TeichAI/Claude-Opus-4.6-Reasoning-887x](https://huggingface.co/datasets/TeichAI/Claude-Opus-4.6-Reasoning-887x) | 887 | Claude Opus 4.6 |
| [TeichAI/claude-4.5-opus-high-reasoning-250x](https://huggingface.co/datasets/TeichAI/claude-4.5-opus-high-reasoning-250x) | 250 | Claude Opus 4.5 |
| [TeichAI/Claude-Sonnet-4.6-Reasoning-1100x](https://huggingface.co/datasets/TeichAI/Claude-Sonnet-4.6-Reasoning-1100x) | 1,100 | Claude Sonnet 4.6 |
| **합계 (필터 후)** | **4,451** | - |

---

## 🎯 설계 철학 (Darwin V8)

1. **LoRA Without Regret** — `all-linear` target, high LR, 작은 rank도 OK
2. **Response Distillation** — pre-generated Opus traces로 비용 효율적 증류
3. **Merge-and-Deploy** — LoRA 어댑터 통합 후 추가 의존성 없이 배포

---

## 🔁 재현 방법

이 모델은 다음 두 컴포넌트를 merge하여 만들어졌습니다:

```python
from transformers import AutoModelForCausalLM
from peft import PeftModel
import torch

base = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3.5-2B", torch_dtype=torch.bfloat16
)
model = PeftModel.from_pretrained(
    base, "FINAL-Bench/Qwen3.5-2B-Opus-Distill-v1"
)
merged = model.merge_and_unload()
merged.save_pretrained("./lastbrain")
```

---

## 📝 샘플 테스트 결과 (4문제)

| 유형 | 정답 여부 | 응답 길이 |
|-----|---------|---------|
| Math (기차 속도) | ✅ 80 km/h | 771자 |
| Logic (키 비교) | ✅ Carol | 354자 |
| Code (소수 판별) | ✅ Python 함수 | 1,712자 |
| Korean (최저시급) | ✅ 1,577,600원 | 142자 |

**Markdown/LaTeX/Step-by-Step 구조화된 답변 자연스럽게 생성**

---

## ⚠️ 제한 사항

- **규모**: 2.3B 파라미터 (소형 모델)
- **한국어 계산 정확성**: 때로 숫자 오류 발생 가능 (소형 모델 한계)
- **긴 컨텍스트**: 학습 시 max_length=4,096으로 학습됨
- **`<think>` 태그**: 명시적 사용 낮음 (reasoning을 본문에 통합)

---

## 🪪 라이선스

- Base model: Apache 2.0 (Qwen)
- 학습 데이터: 각 데이터셋 개별 라이선스 참조
- 이 모델: Apache 2.0

---

## 🙏 크레딧

- **Base**: Qwen team (Alibaba)
- **Teacher**: Anthropic (Claude Opus 4.5/4.6, Sonnet 4.6)
- **데이터 공개**: nohurry, TeichAI
- **Training & Release**: FINAL-Bench / VIDRAFT_LAB

---

## 🔗 관련 모델

- 🧠 [`FINAL-Bench/Qwen3.5-2B-Opus-Distill-v1`](https://huggingface.co/FINAL-Bench/Qwen3.5-2B-Opus-Distill-v1) — 이 모델의 **LoRA 어댑터 단독 버전**
- ⚡ [`FINAL-Bench/Qwen3.5-2B-Opus-SDPO-v1`](https://huggingface.co/FINAL-Bench/Qwen3.5-2B-Opus-SDPO-v1) — Phase 4 SDPO 자기증류 강화본

---

*Darwin V8 · Part of the evolutionary model merging series by VIDRAFT_LAB*