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youngseok12/HyperCLOVA-X-SEED-Think-14B-sft-71875-wrong-correction-minimal

sourceHugging Faceotherupdated 22d agoView on Hugging Face

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HyperCLOVA X SEED Think-14B — AI Hub 71875 Base-wrong Correction Minimal SFT

This repository contains a standalone BF16 model derived from naver-hyperclovax/HyperCLOVAX-SEED-Think-14B. A deliberately minimal LoRA adapter was trained on the Base-wrong correction pool from AI Hub dataset 71875 and merged into the pristine base weights.

Model details

—Base model: naver-hyperclovax/HyperCLOVAX-SEED-Think-14B
—Base revision: 9b74e35d4c7e4ffec489f4171273caca8948a2b9
—Architecture: HyperCLOVAXForCausalLM
—Weight format: BF16 safetensors, standalone merged full model
—Chat template: official HyperCLOVA X template, preserved byte-for-byte
—LoRA: rank 4, alpha 8, dropout 0, bias none
—Target modules: qproj, vproj
—Objective: assistant-token-only causal-language-model cross entropy
—Learning rate: 1e-6; scheduler: constant; warmup: 0; weight decay: 0
—Training: 606 examples, 1 epoch, 38 optimizer steps, effective batch size 16
—Per-device batch: 1; gradient accumulation: 16
—Maximum sequence length: 1024; precision: BF16; packing: false
—Seed: 42; data seed: 42
—Public benchmark data: not used

Training data

The training pool contains 606 rows selected from the 3,000-row AI Hub 71875 K_train source after a pristine Base label probe: the Base model produced an unambiguous wrong answer label on these rows. The pool contains 175 internal-medicine, 177 obstetrics-and-gynecology, 182 pediatrics, and 72 emergency-medicine rows. Uncertain rows (669) were excluded. Training targets are the gold A–D answer labels; the Base model's wrong output is not included as a training target.

The source file SHA-256 is ff13bf66d46102c773d28934239ec347117726c617df74b6443b0763991cfa60. The source dataset is AI Hub 71875 필수의료 의학지식 QA: https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71875

This is an experimental wrong-answer correction arm; it should not be interpreted as a general benchmark result. No benchmark questions, answers, evaluation artifacts, logs, credentials, or .env files are included in this repository. AI Hub source-data terms remain applicable.

Usage

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

modelid = "youngseok12/HyperCLOVA-X-SEED-Think-14B-sft-71875-wrong-correction-minimal" tokenizer = AutoTokenizer.frompretrained(modelid) model = AutoModelForCausalLM.frompretrained( modelid, dtype=torch.bfloat16, devicemap="auto" ) messages = [{"role": "user", "content": "대한민국의 수도는 어디인가요?"}] inputs = tokenizer.applychattemplate( messages, addgenerationprompt=True, tokenize=True, returntensors="pt" ).to(model.device) with torch.inferencemode(): outputs = model.generate(**inputs, maxnewtokens=64, dosample=False) print(tokenizer.decode(outputs[0][inputs["inputids"].shape[-1]:], skipspecialtokens=True)) ~~~

Intended use and limitations

This is an experimental Korean-language fine-tuned model for research and controlled evaluation. It can produce factual or reasoning errors and is not a substitute for professional medical, legal, financial, or other advice. The HyperCLOVA X acceptable-use restrictions and all applicable laws continue to apply to this derivative model.

License and notices

The full HyperCLOVA X SEED 14B Think Model License Agreement is included in LICENSE, and the required NAVER attribution is in NOTICE. Redistribution must comply with that agreement, including its prohibited-use policy and attribution requirements. AI Hub source-data terms also remain applicable.