---
language:
  - ko
  - en
license: gemma
base_model: lablup/gemma-2-2b-it-xaas-qa
tags:
  - gemma2
  - korean
  - trade
  - information-extraction
  - b2b
  - email
  - yaml
  - supply-chain
library_name: transformers
pipeline_tag: text-generation
---

# XaaS Gemma 2 2B — Stage 3: KIE Fine-Tuning (Production Model)

**Stage 3 of 4** in the XaaS fine-tuning pipeline for Korean international trade.

Fine-tuned from the QA model ([lablup/gemma-2-2b-it-xaas-qa](https://huggingface.co/lablup/gemma-2-2b-it-xaas-qa)) for **Key Information Extraction (KIE)** from B2B supply-chain email threads. Given a multi-turn email conversation between a Korean buyer and an overseas supplier, the model extracts structured trade information (contract terms, parties, dates, prices, delivery schedule) as YAML. This is the **production merged model** deployed via vLLM in the XaaS API.

## Pipeline Position

```
google/gemma-2-2b-it
    ↓
lablup/gemma-2-2b-it-xaas-cpt
    ↓
lablup/gemma-2-2b-it-xaas-qa
    ↓  [this model]
lablup/gemma-2-2b-it-xaas-kie  ← you are here  (production)
```

## Training Details

| Parameter | Value |
|-----------|-------|
| Base model | `lablup/gemma-2-2b-it-xaas-qa` |
| Method | Supervised fine-tuning (SFT) with LoRA, then merged |
| LoRA rank (r) | 256 |
| LoRA alpha | 32 |
| LoRA dropout | 0.05 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Learning rate | 2e-3 |
| Max sequence length | 6,000 tokens |
| Batch size (effective) | 64 (2 per GPU × 16 gradient accumulation × 2 nodes) |
| Optimizer | paged_adamw_8bit |
| Precision | bfloat16 |
| Distributed training | DeepSpeed ZeRO-3, 2 nodes |
| Framework | HuggingFace TRL SFTTrainer + DeepSpeed |

The LoRA adapter has been **merged into the base weights**. Load directly with `AutoModelForCausalLM` (no PEFT dependency required).

## Training Data

[**lablup/tariff_trade_domain.synthetic_trade_email_kie_kr**](https://huggingface.co/datasets/lablup/tariff_trade_domain.synthetic_trade_email_kie_kr) — 1,188 synthetic B2B supply-chain email threads, each paired with a structured YAML extraction of:
- 계약 및 조건 (contract terms, payment conditions)
- 참여자 (buyer/supplier parties)
- 날짜 / 이벤트 (dates, key milestones)
- 가격 / 배송 조건 (pricing, delivery schedule)

Generated by GPT-4o-mini across 20 industries (Aerospace, Technology, Manufacturing, Healthcare, ...).

## How to Use

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

model_id = "lablup/gemma-2-2b-it-xaas-kie"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

def extract_kie(email_thread: str) -> str:
    prompt_text = (
        "다음 이메일 대화에서 계약 관련 정보를 YAML 형식으로 추출하세요.\n\n"
        f"{email_thread}"
    )
    messages = [{"role": "user", "content": prompt_text}]
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(**inputs, max_new_tokens=1024, do_sample=False)
    return tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)

email = """
**Buyer Details:**
- Name: 박지훈
- Company: SkyLine Aerospace Ltd.

**Email Exchange:**
From: jihoon.park@skylineaerospace.kr
Subject: 항공용 알루미늄 부품 100개 견적 요청
...
"""
print(extract_kie(email))
# ```yaml
# 계약 및 조건:
#     결제 조건: 배송 시 결제
#     배송 일정: 주문 확인일로부터 2주 이내
# ...
```

### OpenAI-compatible API (vLLM)

```python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="none")
response = client.chat.completions.create(
    model="xaas-gemma-2-2b-it-lora128",
    messages=[{
        "role": "user",
        "content": "다음 이메일 대화에서 계약 관련 정보를 YAML 형식으로 추출하세요.\n\n{email_thread}"
    }],
    max_tokens=1024,
)
print(response.choices[0].message.content)
```

## Production Deployment

Served with vLLM at `--max-model-len 8128` and `--tensor-parallel-size 1`. Model weights are in float16, ~5 GB.

## Expected Output Format

```yaml
계약 및 조건:
    결제 조건: 선불 50%, 잔금 배송 시
    배송 일정: 계약 체결 후 4주
    보증: 12개월
참여자:
    구매자: 박지훈, SkyLine Aerospace Ltd.
    공급업체: GlobalParts Inc.
날짜:
    문의일: 2024-07-26
    예상 납기: 2024-08-23
이벤트:
    - 초기 문의 및 사양 확인
    - 가격 협상 (10% 대량 할인 적용)
    - 최종 계약 합의
```

## Limitations

- Training data is LLM-generated; extraction accuracy on real emails has not been independently verified
- YAML schema is fixed to the training format; highly irregular email structures may produce incomplete extractions
- Optimized for Korean-buyer / English-supplier email threads; pure Korean or pure English threads may work but were less represented in training

## License

Built on [Google Gemma 2](https://huggingface.co/google/gemma-2-2b-it) and subject to the [Gemma Terms of Use](https://ai.google.dev/gemma/terms).