youngseok12/AX-3.1-Light-sft_v3_1_A_control

AX-3.1-Light-sftv31Acontrol

This is a standalone BF16 model obtained by fine-tuning `skt/A.X-3.1-Light` with a LoRA adapter and merging the adapter into the base weights. It is intended for Korean-language research and controlled evaluation. The repository contains no benchmark data, benchmark answers, training logs, or access credentials.

Model Details

• —Base model: skt/A.X-3.1-Light
• —Base revision used for training and merge: 9b41bb2406472634d8812c0b8931fa40fa9a6c3a
• —Architecture: unchanged from the base model
• —Weight format: BF16 safetensors
• —Chat template: official A.X tokenizer chat template
• —Custom Python model code: none
• —Submission form: merged full model; no separate adapter is required
• —Experiment condition: Control run for measuring the effect of the alternative v3.1 mixtures.

Training Data

The training split contains 36,000 examples and the AI Hub validation-derived development split contains 3,000 examples. Source-level train/dev separation and exact-duplicate checks were retained. The source domains were:

• —Civil law LLM instruction-tuning data
• —Criminal law LLM instruction-tuning data
• —Administrative law LLM instruction-tuning data
• —Corporate accounting standards data
• —Essential medical knowledge data
• —News article machine-reading data
• —CoT-Fabric technology valuation data

The v3.0 clean 36,000-example mixture was retained as the control condition. 55.80% of assistant target tokens (3,000 reasoning examples).

Public evaluation benchmarks such as KMMLU-Pro, CLIcK, HLE, SNU Ko-MuSR, Com2-main, and Original MuSR were not used as SFT data.

Training Procedure

• —Objective: standard assistant-token causal-language-model cross entropy
• —Epochs: 1
• —Learning rate: 3e-5
• —Scheduler: cosine with 0.03 warmup ratio
• —Weight decay: 0.01
• —Maximum gradient norm: 1.0
• —LoRA: rank 16, alpha 32, dropout 0.05
• —LoRA target modules: q_proj, k_proj, v_proj, o_proj
• —Effective batch size: 32
• —Maximum sequence length: 2048
• —Precision: BF16
• —Packing: disabled
• —Random seed: 20260827

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "youngseok12/AX-3.1-Light-sft_v3_1_A_control"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, dtype=torch.bfloat16, device_map="auto"
)
messages = [{"role": "user", "content": "대한민국의 수도는 어디인가요?"}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=True, return_tensors="pt"
).to(model.device)
with torch.inference_mode():
    outputs = model.generate(**inputs, max_new_tokens=64, do_sample=False)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

For an OpenAI-compatible deployment, the merged repository is intended to be loadable directly by standard vLLM without an adapter or trust_remote_code. Run the K-AI submission compatibility checks separately before submission.

Intended Use and Limitations

This model is an experimental Korean SFT model for research and controlled evaluation. It can produce factual errors and should not be used as a substitute for professional legal, accounting, medical, or financial advice. The AI Hub source data remains subject to its original access terms.

License

The base model is distributed under the Apache License 2.0. The applicable terms of the AI Hub source data remain in force for use of the training data. See LICENSE for the base model license text.