Instructions to use youngseok12/AX-3.1-Light-minimal-sft-71875 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use youngseok12/AX-3.1-Light-minimal-sft-71875 with Transformers:

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

pipe = pipeline("text-generation", model="youngseok12/AX-3.1-Light-minimal-sft-71875")
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-minimal-sft-71875")
model = AutoModelForCausalLM.from_pretrained("youngseok12/AX-3.1-Light-minimal-sft-71875", 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-minimal-sft-71875 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-minimal-sft-71875"
# 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-minimal-sft-71875",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/youngseok12/AX-3.1-Light-minimal-sft-71875

SGLang

How to use youngseok12/AX-3.1-Light-minimal-sft-71875 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-minimal-sft-71875" \
    --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-minimal-sft-71875",
		"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-minimal-sft-71875" \
        --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-minimal-sft-71875",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use youngseok12/AX-3.1-Light-minimal-sft-71875 with Docker Model Runner:
```
docker model run hf.co/youngseok12/AX-3.1-Light-minimal-sft-71875
```

A.X-3.1-Light — Minimal Self-Distillation SFT (AI Hub 71875)

This repository contains a standalone BF16 model derived from skt/A.X-3.1-Light. It reproduces — on a different base model — the same minimal self-distillation recipe used by our best-performing HyperCLOVA X SEED Think-14B submission, to test whether the recipe's real-leaderboard result transfers across base models.

Model details

Base model: skt/A.X-3.1-Light
Base revision: 9b41bb2406472634d8812c0b8931fa40fa9a6c3a
Architecture: LlamaForCausalLM
Weight format: BF16 safetensors, standalone merged full model
Chat template: official A.X-3.1-Light template, preserved byte-for-byte
LoRA: rank 4, alpha 8, dropout 0, target modules q_proj, v_proj
Objective: assistant-token causal-language-model cross entropy (label-only)
Learning rate: 1e-6, constant scheduler, no warmup or weight decay
Training: 512 examples, 1 epoch, 32 optimizer steps, effective batch 16
Maximum sequence length: 1024; precision: BF16; seed: 42
Public benchmark data: not used

Data

The pristine base model was run against a 950-item eligible pool of AI Hub 71875 필수의료 (essential medical) QA training rows with an auditable single-choice gold label. 539 of 950 items (56.7%) produced an unambiguous free-form answer that matched the AI Hub gold label; the first 512 of those (round-robin across domains, seed 42) were kept. Each training target is the base model's own generated output for that item (self-distillation), not a human-authored label. No benchmark questions, answers, evaluation artifacts, logs, credentials, or .env files are included in this repository.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "youngseok12/AX-3.1-Light-minimal-sft-71875"
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))

Intended use and limitations

This is an experimental Korean-language fine-tuned model for research and controlled evaluation. It may produce factual or reasoning errors and is not a substitute for professional legal, medical, financial, or other advice. The A.X acceptable-use restrictions and all applicable laws continue to apply to this derivative model.

License and notices

The A.X-3.1-Light model, and this derivative, are licensed under the Apache License, Version 2.0; the full text is included in LICENSE. AI Hub source-data terms also remain applicable.

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Safetensors

Model size

7B params

Tensor type

BF16

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