lablup/gemma-2-2b-it-xaas-sum-tag
XaaS Gemma 2 2B — Stage 4: Summarization + Tag Fine-Tuning
Stage 4 of 4 in the XaaS fine-tuning pipeline for Korean international trade.
A LoRA adapter (r=256) fine-tuned from the QA model (lablup/gemma-2-2b-it-xaas-qa) for summarization and topic tagging of B2B supply-chain email threads. Given a multi-turn email conversation between a Korean buyer and an overseas supplier, the model generates a Korean prose summary and a list of 5–8 Korean topic tags.
Pipeline Position
google/gemma-2-2b-it
↓
lablup/gemma-2-2b-it-xaas-cpt
↓
lablup/gemma-2-2b-it-xaas-qa
↓ [this model — LoRA adapter]
lablup/gemma-2-2b-it-xaas-sum-tag ← you are hereTraining Details
Training Data
**lablup/tariff_trade_domain.synthetic_trade_email_sum_tag_kr** (sum_tag config, Korean summaries) — 1,188 synthetic B2B supply-chain email threads paired with:
- Korean prose summaries (
summary) - 5–8 Korean topic tags (
tags, e.g.["공급망", "협상", "건설", "계약"])
Generated by GPT-4o-mini across 20 industries and 17 conversation styles.
How to Use
This is a LoRA adapter — load it with PEFT on top of the base QA model:
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
base_model_id = "lablup/gemma-2-2b-it-xaas-qa"
adapter_id = "lablup/gemma-2-2b-it-xaas-sum-tag"
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model = PeftModel.from_pretrained(base_model, adapter_id)
def summarize_and_tag(conversation: str) -> str:
prompt_text = f"대화를 요약하고 태그를 생성해줘:\n{conversation}"
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=512, do_sample=False)
return tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
conversation = """
보낸 사람: minji.kim@aeropack.co.kr
수신: sales@packagingworld.com
제목: 맞춤형 포장 솔루션 견적 요청
...
"""
output = summarize_and_tag(conversation)
print(output)
# tags:["협상", "계약", "포장", "공급업체", "물류"] <sep> summary:AeroPack Solutions와 공급업체 간의 ...Parse structured output
def parse_output(raw: str) -> dict:
tags_part, summary_part = raw.split(" <sep> ")
import json
tags = json.loads(tags_part.replace("tags:", "").strip())
summary = summary_part.replace("summary:", "").strip()
return {"tags": tags, "summary": summary}Output Format
The model outputs tags first, then a summary, separated by <sep> :
tags:["공급망", "거래협상", "견적요청", "납품조건", "결제"] <sep> summary:한국사무용품과 Global Staples Inc. 간의 공급망 상호작용으로, 초기 문의 및 긴급성, 수량 협상, 납품 일정, 결제 조건이 논의되었으며 최종 합의에 도달하였습니다.Merging the Adapter (optional)
To produce a standalone model without PEFT dependency:
merged = model.merge_and_unload()
merged.save_pretrained("gemma-2-2b-it-xaas-sum-tag-merged")
tokenizer.save_pretrained("gemma-2-2b-it-xaas-sum-tag-merged")Limitations
- Summaries and tags are generated for Korean-language conversations; English-only threads may work but were underrepresented in training
- Tag vocabulary reflects the 20 industry categories in the training dataset
- Summary length and detail level depend on conversation length; very short conversations may produce sparse summaries
License
Built on Google Gemma 2 and subject to the Gemma Terms of Use.