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Copyright (c) 2025 SK Telecom Co., Ltd. All rights reserved.
Unless otherwise stated, all files in this repository (including modified model weights
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---
base_model: skt/A.X-3.1-Light
library_name: transformers
language:
- ko
license: apache-2.0
pipeline_tag: text-generation
tags:
- korean
- sft
- lora-merged
- safetensors
- k-ai-leaderboard
---
# A.X-3.1-Light-sft_v3_0
`skt/A.X-3.1-Light`를 기반으로 AI Hub 한국어 데이터로 수행한 첫 번째
clean SFT baseline입니다. LoRA adapter를 base model에 병합한
standalone BF16 모델이므로 별도의 adapter나 custom Python code 없이
Transformers 및 표준 vLLM에서 로드할 수 있습니다.
이 모델은 연구 및 2026 K-DATA SCIENCE 해커톤의 로컬 검증을 위한
실험 산출물입니다. K-AI 리더보드의 공식 점수를 포함하지 않으며, 공개
benchmark 문항은 학습 데이터에 포함하지 않았습니다.
## Model Details
- Base model: [`skt/A.X-3.1-Light`](https://huggingface.co/skt/A.X-3.1-Light)
- Architecture: `LlamaForCausalLM` (unchanged from the base model)
- Parameters: approximately 7B
- Weight format: BF16 `safetensors`
- Chat template: the official A.X chat template
- Custom code: none
- Merge method: PEFT LoRA `merge_and_unload`
- Base revision used for training and merge: `9b41bb2406472634d8812c0b8931fa40fa9a6c3a`
## Training Data
The SFT view contained 36,000 training examples and 3,000 validation
examples. Training and validation were kept separate at the source level.
The following AI Hub data domains were used:
- Civil law LLM pretraining and instruction-tuning data
- Criminal law LLM pretraining and instruction-tuning data
- Administrative law LLM pretraining and instruction-tuning data
- Corporate accounting standards data
- Essential medical knowledge data
- News article machine-reading data
- CoT-Fabric technology valuation data
Public evaluation benchmark data, including KMMLU-Pro, CLIcK, HLE,
SNU Ko-MuSR, Com2-main, and Original MuSR, was not used for SFT.
## Training Procedure
- Objective: standard assistant-token causal language-model cross entropy
- Epochs: 1
- Learning rate: `5e-5`
- Scheduler: cosine
- Warmup ratio: `0.03`
- Weight decay: `0.01`
- LoRA rank: `16`
- LoRA alpha: `32`
- LoRA 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: `20260825`
The final training view used 800 reasoning examples. Their output tokens
accounted for approximately 24.37% of the total assistant target tokens.
The training format validator passed all 36,000 training and 3,000
validation examples.
## Usage with Transformers
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "youngseok12/AX-3.1-Light-sft_v3_0"
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))
```
## Usage with vLLM
```bash
vllm serve youngseok12/AX-3.1-Light-sft_v3_0 \
--dtype bfloat16 \
--max-model-len 32768
```
The repository is intended to be publicly accessible and directly loadable
by a standard vLLM OpenAI-compatible server. The model does not require
`trust_remote_code=True`.
## Intended Use and Limitations
This model is intended for Korean-language research, controlled evaluation,
and experimentation with instruction-following and domain-oriented SFT.
It is not a substitute for professional legal, accounting, or medical
advice. Responses may contain factual errors, unsupported reasoning, or
format deviations. The reported training configuration does not imply an
official K-AI leaderboard result.
The AI Hub source data remains subject to its original access terms and
licenses. This repository contains model weights and documentation, not the
source training datasets.
## License
The base model is distributed under the Apache License 2.0. Please review
the base model license and the terms applicable to the source training data
before using or redistributing this derivative model.

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{%- if tools is iterable and tools | length > 0 %}
{{- '<|im_start|><|system|>'}}
{{- '당신은 도구 호출 기능을 갖춘 유용한 도우미입니다. 사용자의 요청을 처리하기 위해서 필요한 도구가 주어진 목록에 있는 경우 도구 호출로 응답하세요.
필요한 도구가 목록에 없는 경우에는 도구 호출 없이 사용자가 요구한 정보를 제공하세요.
필요한 도구가 목록에 있지만 해당 도구를 호출하는데 필요한 argument 정보가 부족한 경우 해당 정보를 사용자에게 요청하세요.
사용자의 요청을 처리하기 위해 여러번 도구를 호출할 수 있어야 합니다.
도구 호출 이후 도구 실행 결과를 입력으로 받으면 해당 결과를 활용하여 답변을 생성하세요.
다음은 접근할 수 있는 도구들의 목록 입니다:
<tools>
'}}
{%- for t in tools %}
{{- t | tojson }}
{{- '
' }}
{%- endfor %}
{{- '</tools>' }}
{{- '
도구를 호출하려면 아래의 JSON으로 응답하세요.
도구 호출 형식: <tool_call>{"name": 도구 이름, "arguments": dictionary 형태의 도구 인자값}</tool_call>' }}
{{- '<|im_end|>' }}
{%- endif %}
{%- for message in messages %}
{%- if message.role == 'system' %}
{{- '<|im_start|><|system|>' + message.content + '<|im_end|>'}}
{%- elif message.role == 'user' %}
{{- '<|im_start|><|user|>' + message.content + '<|im_end|>'}}
{%- elif message.role == 'assistant' %}
{{- '<|im_start|><|assistant|>'}}
{%- set content = '' %}
{%- if message.content is defined %}
{%- set content = message.content %}
{%- endif %}
{%- if add_generation_prompt and not (message.reasoning_content is defined and message.reasoning_content is not none) %}
{%- if '</think>' in message.content %}
{%- set content = message.content.split('</think>'.strip())[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{{- content}}
{%- if message.tool_calls is defined %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>' }}
{{- '{' }}
{{- '"name": "' }}
{{- tool_call.name }}
{{- '"' }}
{%- if tool_call.arguments is defined %}
{{- ', ' }}
{{- '"arguments": ' }}
{{- tool_call.arguments|tojson }}
{%- endif %}
{{- '}' }}
{{- '</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>'}}
{%- elif message.role == 'tool' %}
{{- '<|im_start|><|extra_id_13|><tool_output>' + message.content + '</tool_output><|im_end|>'}}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|><|assistant|>' }}
{%- endif %}

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.1,
"bos_token_id": 0,
"dtype": "bfloat16",
"eos_token_id": 0,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 10880,
"max_position_embeddings": 32768,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 32,
"pad_token_id": null,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 500000,
"rope_type": "default"
},
"tie_word_embeddings": false,
"transformers_version": "5.15.0",
"use_cache": false,
"vocab_size": 102400
}

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{
"bos_token_id": 0,
"eos_token_id": 27,
"max_new_tokens": 32768,
"pad_token_id": 1,
"transformers_version": "5.15.0"
}

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{
"model_name": "A.X-3.1-Light-sft_v3_0",
"base_model": "skt/A.X-3.1-Light",
"base_revision": "9b41bb2406472634d8812c0b8931fa40fa9a6c3a",
"finetuning_method": "LoRA SFT followed by merge_and_unload",
"merge_output": "standalone BF16 safetensors",
"custom_code_required": false,
"training": {
"train_examples": 36000,
"validation_examples": 3000,
"epochs": 1,
"learning_rate": 0.00005,
"scheduler": "cosine",
"warmup_ratio": 0.03,
"weight_decay": 0.01,
"effective_batch_size": 32,
"max_sequence_length": 2048,
"lora_rank": 16,
"lora_alpha": 32,
"lora_dropout": 0.05,
"target_modules": ["q_proj", "k_proj", "v_proj", "o_proj"],
"precision": "bfloat16",
"packing": false,
"seed": 20260825,
"loss": "standard assistant-token causal LM cross entropy"
},
"training_data": {
"source": "AI Hub domain-specific Korean datasets",
"public_benchmark_data_included": false,
"train_validation_source_overlap": 0,
"train_examples_by_output_contract": {
"EXPLANATION": 19643,
"SHORT_ANSWER": 7583,
"NUMBER_ONLY": 5176,
"LABEL_ONLY": 2798,
"REASONING_FINAL": 800
},
"reasoning_final_output_token_share": 0.243691
},
"created_at_utc": "2026-08-26T10:46:37Z"
}

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version https://git-lfs.github.com/spec/v1
oid sha256:e177650628923e6ab41e67ce2a0046010ab74a9288d51b151f685f116c911b6f
size 14529635608

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{
"additional_special_tokens": [
"<|endoftext|>",
"<|pad|>",
"<|unk|>",
"<|sep|>",
"<|mask|>",
"<|cls|>",
"<|image|>",
"<|audio|>",
"<|user|>",
"<|system|>",
"<|assistant|>",
"<|extra_id_0|>",
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"<|im_end|>",
"<|resident_reg|>",
"<|foreigner_reg|>",
"<|business_reg|>",
"<|credit_card|>",
"<|passport|>",
"<|driver_license|>",
"<|telephone|>",
"<|health_insurance|>",
"<|bank_account|>"
],
"bos_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"cls_token": {
"content": "<|cls|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "<|im_end|>",
"lstrip": false,
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},
"pad_token": {
"content": "<|pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"sep_token": {
"content": "<|sep|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<|unk|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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tokenizer_config.json Normal file
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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": "<|endoftext|>",
"clean_up_tokenization_spaces": true,
"cls_token": "<|cls|>",
"eod_token": "<|endoftext|>",
"eos_token": "<|im_end|>",
"errors": "replace",
"is_local": true,
"local_files_only": true,
"mask_token": "<|mask|>",
"max_length": 7680,
"model_max_length": 32768,
"model_specific_special_tokens": {
"eod_token": "<|endoftext|>"
},
"pad_token": "<|pad|>",
"sep_token": "<|sep|>",
"tokenizer_class": "GPT2Tokenizer",
"unk_token": "<|unk|>",
"vocab_size": 102400
}

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