Instructions to use youngseok12/AX-4.0-Light-sft_71949_text_causal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use youngseok12/AX-4.0-Light-sft_71949_text_causal with Transformers:

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

pipe = pipeline("text-generation", model="youngseok12/AX-4.0-Light-sft_71949_text_causal")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("youngseok12/AX-4.0-Light-sft_71949_text_causal")
model = AutoModelForCausalLM.from_pretrained("youngseok12/AX-4.0-Light-sft_71949_text_causal", 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-4.0-Light-sft_71949_text_causal with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "youngseok12/AX-4.0-Light-sft_71949_text_causal"
# 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-4.0-Light-sft_71949_text_causal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/youngseok12/AX-4.0-Light-sft_71949_text_causal

SGLang

How to use youngseok12/AX-4.0-Light-sft_71949_text_causal 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-4.0-Light-sft_71949_text_causal" \
    --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-4.0-Light-sft_71949_text_causal",
		"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-4.0-Light-sft_71949_text_causal" \
        --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-4.0-Light-sft_71949_text_causal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use youngseok12/AX-4.0-Light-sft_71949_text_causal with Docker Model Runner:
```
docker model run hf.co/youngseok12/AX-4.0-Light-sft_71949_text_causal
```

A.X-4.0-Light SFT — 71949 Text Causal

This repository contains a standalone BF16 full-weight model derived from skt/A.X-4.0-Light. A LoRA adapter was trained on a carefully filtered, text-only subset of AI Hub Dataset 71949 (causal reasoning) and then merged into the base weights. The model is intended for Korean-language research and controlled benchmark experiments. It can produce factual or reasoning errors and is not a substitute for professional advice.

Model details

Base model: skt/A.X-4.0-Light
Architecture: Qwen2-family causal language model (base architecture unchanged)
Weight format: BF16 safetensors, standalone merged full model
LoRA: rank 16, alpha 32, dropout 0.05; targets q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Objective: assistant-token causal-language-model cross entropy; system and user tokens masked
Training: 478 train examples, 50 held-out development examples, 2 epochs
Optimizer settings: learning rate 5e-5, linear scheduler, no warmup, weight decay 0
Sequence length: 2048; per-device batch 1; gradient accumulation 8; effective batch 8
Precision: BF16; packing disabled; seed 20260901
Chat template: official A.X tokenizer template preserved in chat_template.jinja
Submission form: merged full model; no separate adapter or custom Python model code is required

Training data

The only training source was AI Hub Dataset 71949 — Causal Reasoning. The source contains image-grounded labels, so image files were not downloaded or used. Records whose question or answer required an image/photo/drawing were excluded. Evidence-only visual wording was normalized to text, the third conclusion step was omitted to reduce answer leakage, and exact questions were deduplicated.

From 16,000 source labels, 530 candidates were obtained and reduced to 528 unique question/source records. The final split contains 478 training and 50 development records. No public benchmark questions, answers, evaluation files, credentials, or .env files are included in this repository. Use of the AI Hub source remains subject to its original terms.

Local evaluation

The canonical local suite was run with deterministic free and B1_constrained probes. The following are B1 parsed-accuracy results, not official K-AI leaderboard scores:

Benchmark	Parsed accuracy
KMMLU-Pro	47.27%
CLIcK	67.37%
HLE text-only	3.75%
SNU Ko-MuSR	48.93%
Com2-main	50.60%
Original MuSR (supplementary)	55.56%

The five-axis local mean (KMMLU-Pro, CLIcK, HLE text-only, SNU Ko-MuSR, and Com2-main) is 43.59%. The full run produced zero generation errors.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "jwg0830/AX-4.0-Light-sft_71949_text_causal"
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))

The merged repository is designed to load with standard Transformers or vLLM without trust_remote_code and without attaching a separate adapter.

License and limitations

The base model and the merged model files are distributed under the Apache License 2.0 as provided by SK Telecom; see LICENSE. The AI Hub source-data terms continue to apply. This is an experimental model for research and controlled evaluation, not a medical, legal, financial, or other professional advice system.

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Safetensors

Model size

7B params

Tensor type

BF16

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skt/A.X-4.0-Light

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