AX-3.1-Light-sft_v3_1_E_format_safe
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: Format-stability comparison condition focused on short and structured targets.
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
24,000 existing non-reasoning examples and 12,000 additional AI Hub examples; reasoning examples were excluded from training. 0% of assistant target tokens in the training split.
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
0.03warmup ratio - Weight decay:
0.01 - Maximum gradient norm:
1.0 - LoRA: rank
16, alpha32, dropout0.05 - 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_E_format_safe"
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.