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colli98/
qwen3-1.7B-ko-summary-finetuned

Name: qwen3-1.7B-ko-summary-finetuned
Availability: InStock

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qwen3-1.7B-ko-summary-finetuned

A fine-tuned Qwen3-1.7B model specialized for abstractive summarization of Korean documents, particularly academic papers. This model was trained on high-quality Korean paper summarization data to generate concise, coherent abstracts from long-form Korean texts.

Model Description

Architecture: Qwen3-1.7B
Fine-tuning Task: Abstractive summarization
Training Data: Korean academic paper summaries (e.g., KoreaScience dataset)

Intended Use

Summarizing long Korean documents—especially research papers—into clear, concise overviews.
Integrating into research tools, educational platforms, or automated document-processing pipelines.

Limitations & Risks

May produce inaccuracies or hallucinated content.
Not intended for generating verbatim legal/medical texts or for extractive quotation.
Users should verify critical facts against original sources.

Installation

pip install transformers safetensors

Usage

python

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("your-username/qwen3-1.7B-ko-summary-finetuned")
model = AutoModelForSeq2SeqLM.from_pretrained("your-username/qwen3-1.7B-ko-summary-finetuned")

text = "여기에 긴 한국어 논문 텍스트를 입력하세요..."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="longest")
summary_ids = model.generate(
    **inputs,
    max_length=150,
    num_beams=4,
    early_stopping=True
)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(summary)

Files in This Repository

bash

.
├── config.json
├── generation_config.json
├── model.safetensors
├── model.safetensors.index.json
├── tokenizer.json
├── tokenizer_config.json
├── special_tokens_map.json
├── vocab.json
├── merges.txt
└── added_tokens.json

colli98/qwen3-1.7B-ko-summary-finetuned