初始化项目，由ModelHub XC社区提供模型

Model: youngseok12/AX-3.1-Light-sft_v3_0
Source: Original Platform

README.md

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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.
+