Instructions to use youngseok12/AX-3.1-Light-sft_source_screen_71875_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_71875_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_71875_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_71875_3000")
model = AutoModelForCausalLM.from_pretrained("youngseok12/AX-3.1-Light-sft_source_screen_71875_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_71875_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_71875_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_71875_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_71875_3000

SGLang

How to use youngseok12/AX-3.1-Light-sft_source_screen_71875_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_71875_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_71875_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_71875_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_71875_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_71875_3000 with Docker Model Runner:
```
docker model run hf.co/youngseok12/AX-3.1-Light-sft_source_screen_71875_3000
```

A.X-3.1-Light SFT Source Screen 71875 (Essential Medical 3K)

이 모델은 skt/A.X-3.1-Light를 기반으로 AI Hub 71875 필수의료 의학지식 데이터만 사용해 한국어 질의응답을 LoRA 방식으로 1 epoch 지도학습한 모델입니다. 학습이 끝난 뒤 LoRA adapter를 base model에 병합한 BF16 standalone 전체 가중치 모델이므로 추론 시 별도의 adapter가 필요하지 않습니다. 연구 및 통제된 평가용 모델이며, 의료 관련 답변을 포함한 모든 생성 결과는 오류가 있을 수 있어 전문적인 판단을 대체할 수 없습니다.

Model details

Model name: A.X-3.1-Light SFT Source Screen 71875 (Essential Medical 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 71875, 필수의료 의학지식 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.

Source	Training examples
Category 14	895
Category 15	895
Category 16	315
Category 17	895
Total	3,000

The output contract is answer-first (정답: ...). Depending on the source question, the target is a label, number, short answer, or concise explanation. Examples over 2,048 chat-template tokens were excluded rather than truncated; the training summary reports zero runtime truncation. The applicable AI Hub terms of use remain in force. AI Hub dataset 71875

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: 10.23 tokens; median: 5 tokens
Total supervised target tokens: 30,703
Final training loss: 0.5942968483

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "youngseok12/AX-3.1-Light-sft_source_screen_71875_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 has not been validated as a medical device or professional medical advice system and must not be used as the sole basis for clinical decisions.

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for youngseok12/AX-3.1-Light-sft_source_screen_71875_3000

Base model

skt/A.X-3.1-Light

Finetuned

(22)

this model