Instructions to use youngseok12/AX-3.1-Light-sft_source_screen_71890_3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use youngseok12/AX-3.1-Light-sft_source_screen_71890_3000 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="youngseok12/AX-3.1-Light-sft_source_screen_71890_3000")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("youngseok12/AX-3.1-Light-sft_source_screen_71890_3000")
model = AutoModelForCausalLM.from_pretrained("youngseok12/AX-3.1-Light-sft_source_screen_71890_3000", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use youngseok12/AX-3.1-Light-sft_source_screen_71890_3000 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "youngseok12/AX-3.1-Light-sft_source_screen_71890_3000"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "youngseok12/AX-3.1-Light-sft_source_screen_71890_3000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/youngseok12/AX-3.1-Light-sft_source_screen_71890_3000

SGLang

How to use youngseok12/AX-3.1-Light-sft_source_screen_71890_3000 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "youngseok12/AX-3.1-Light-sft_source_screen_71890_3000" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "youngseok12/AX-3.1-Light-sft_source_screen_71890_3000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "youngseok12/AX-3.1-Light-sft_source_screen_71890_3000" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "youngseok12/AX-3.1-Light-sft_source_screen_71890_3000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use youngseok12/AX-3.1-Light-sft_source_screen_71890_3000 with Docker Model Runner:
```
docker model run hf.co/youngseok12/AX-3.1-Light-sft_source_screen_71890_3000
```

A.X-3.1-Light SFT Source Screen 71890 (Law, Science, Mathematics 3K)

이 모델은 skt/A.X-3.1-Light를 기반으로 AI Hub 71890의 법·세무·행정, 과학·기술, 수학 문제 및 풀이 데이터를 사용해 한국어 질의응답과 지시 수행을 LoRA 방식으로 1 epoch 지도학습한 모델입니다. 학습 후 LoRA adapter를 base model에 병합한 BF16 standalone 전체 가중치 모델이므로 추론 시 별도의 adapter가 필요하지 않습니다. 긴 설명형 응답이 포함된 학습 데이터의 특성상 답변의 사실성·간결성·형식이 항상 보장되지 않으며, 연구 및 평가 목적으로만 사용해야 합니다.

Model details

Model name: A.X-3.1-Light SFT Source Screen 71890 (Law, Science, Mathematics 3K)
Base model: skt/A.X-3.1-Light
Base model revision: 9b41bb2406472634d8812c0b8931fa40fa9a6c3a
Fine-tuning: LoRA supervised fine-tuning, merged into base weights
Weight format: BF16 safetensors
Architecture: unchanged Llama causal language model architecture
Chat template: bundled A.X tokenizer chat template
Custom model code: none; standard Transformers/vLLM loading is intended

Training data

Training used only AI Hub dataset 71890, AI 파운데이션 모델 LLM/LAM 사후학습용 데이터. The training split contains 3,000 selected examples and the separate development split contains 300 examples. No v0.21 mixture, other AI Hub dataset, public benchmark question, benchmark answer, or evaluation artifact was used as SFT data or included in this repository.

Domain	Training examples	Main subjects
법·세무·행정 관련 질의/지시 응답	1,000	세법 394, 행정법 384, 일반법 222
과학·기술 관련 질의/지시 응답	1,000	생명과학·화학·제조공학·물리·지구과학 각 200
수학 문제 및 풀이	1,000	미적분·수론·대수·조합론 각 250
Total	3,000

The output contract is answer-first: the core answer is presented first and a short rationale may follow. New examples were selected and filtered for a 2,048-token maximum without runtime target truncation. The applicable AI Hub terms of use remain in force. AI Hub dataset 71890

Training configuration

Epochs: 1
Optimizer steps: 375
Maximum sequence length: 2,048
Precision: BF16
Per-device batch size: 1
Gradient accumulation: 8 (effective batch size 8)
Learning rate: 5e-5
Scheduler: cosine; warmup ratio 0.03 (11 steps)
Weight decay: 0.01
Random seed: 42
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
Mean target length: 373.24 tokens; median: 369 tokens
Total supervised target tokens: 1,119,712
Final training loss: 1.2575108846

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "youngseok12/AX-3.1-Light-sft_source_screen_71890_3000"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

Use the bundled tokenizer chat template for conversational inference. The repository is a merged full model and does not require PEFT adapter loading.

Limitations and license

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

The model can produce incorrect, incomplete, biased, or poorly formatted answers. It must not be used as the sole basis for legal, tax, administrative, scientific, mathematical, financial, or other high-impact decisions.

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for youngseok12/AX-3.1-Light-sft_source_screen_71890_3000

Base model

skt/A.X-3.1-Light

Finetuned

(23)

this model