youngseok12

AX-3.1-Light-sft_v3_2_aihub_extension

README

License: apache-2.0

Model information

Model name: A.X-3.1-Light SFT v3.2 AI Hub Extension
Base model: skt/A.X-3.1-Light
Base model revision: 9b41bb2406472634d8812c0b8931fa40fa9a6c3a
Fine-tuning method: LoRA supervised fine-tuning, merged for inference
Model format: standalone BF16 safetensors; no separate adapter is required
Intended use: Korean text generation, instruction following, and research evaluation

Training

Training data file: format_sft_answer_first_extension_13801.jsonl
Selected rows: 13,801
Serialized training examples: 13,793 (8 rows skipped during example construction)
Epochs: 1
Maximum sequence length: 2,048
Precision: BF16
Learning rate: 5e-5
Effective batch size: 8
Scheduler: linear; warmup steps: 0
Weight decay: 0
LoRA rank / alpha / dropout: 16 / 32 / 0.05
LoRA target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Objective: assistant-token causal language-model cross entropy
Training loss: 1.0445830774
Training runtime: 4,646.3862 seconds

The target contract is answer-first: a compact answer is given first, followed by at most one short rationale when a rationale is available. New AI Hub candidates were filtered to fit the base model with a 2,048-token maximum.

Training data

The final training mixture contains 5,801 examples inherited from the v0.21 answer-first core and 8,000 newly selected examples from three AI Hub sources. The source dataset IDs and selected counts are listed below. The source data, benchmark questions, answers, and evaluation artifacts are not included in this repository. Users must follow the applicable AI Hub terms of use.

Table with columns: AI Hub dataset, Description, Selected examples
AI Hub dataset	Description	Selected examples
569	행정 문서 대상 기계독해 데이터 / VL_multiple_choice	2,787
71610	금융·법률 문서 기계독해 데이터 / VL_4 다지선다	644
71857	국어 교과 지문형 문제 데이터	597
71874	전문 의학지식 데이터	823
71890	AI 파운데이션 모델 LLM/LAM 사후학습용 데이터	3,000
71894	지식·지능 데이터

The new-data selection was deterministic with seed 20260831. It covered law, science/technology, mathematics, general knowledge, Korean culture/history, math, science, and social studies categories according to the selection manifest used for this run. Prompt-level duplicate checking found no duplicate prompts or replacements; inherited duplicate SFT IDs may remain as recorded in the manifest.

AI Hub references:

Usage

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "youngseok12/AX-3.1-Light-sft_v3_2_aihub_extension"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

The bundled tokenizer includes the chat template used by the base model. For chat-style inference, use tokenizer.apply_chat_template and keep the prompt format consistent with the intended conversational interface.

Reproducibility

Local adapter checkpoint: /home/youngseok3/KDS/checkpoints/aihub_extension_20260831_gpu3
Local merged output: /home/youngseok3/KDS/submission/AX-3.1-Light-sft_v3_2_aihub_extension-merged
Dataset selection manifest: data/processed/aihub_extension_20260831/selection_manifest.json
Base model revision and merge details are recorded in kds_merge_info.json.

Limitations and license

This model is derived from the Apache-2.0 licensed skt/A.X-3.1-Light model. The base model's notices and SK Telecom trademark terms also apply. AI Hub dataset terms apply to the source data. See LICENSE and the base model repository for the applicable terms.

The model may produce incorrect, incomplete, biased, or poorly formatted answers. It has not been validated as a professional medical, legal, financial, or safety-critical system.

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Model Details

Model Provider

youngseok12

Model Tree

Base

skt/A.X-3.1-Light

Fine-tuned

this model

Input Modalities

Text

Output Modalities