Instructions to use skt/A.X-4.0-Light with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use skt/A.X-4.0-Light with Transformers:

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

pipe = pipeline("text-generation", model="skt/A.X-4.0-Light")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("skt/A.X-4.0-Light")
model = AutoModelForCausalLM.from_pretrained("skt/A.X-4.0-Light", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use skt/A.X-4.0-Light with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "skt/A.X-4.0-Light"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "skt/A.X-4.0-Light",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/skt/A.X-4.0-Light

SGLang

How to use skt/A.X-4.0-Light 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 "skt/A.X-4.0-Light" \
    --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": "skt/A.X-4.0-Light",
		"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 "skt/A.X-4.0-Light" \
        --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": "skt/A.X-4.0-Light",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use skt/A.X-4.0-Light with Docker Model Runner:
```
docker model run hf.co/skt/A.X-4.0-Light
```

liveseongho commited on Jul 23, 2025

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@@ -52,9 +52,6 @@ SK Telecom released **A.X 4.0** (pronounced "A dot X"), a large language model (
 - **Deployment Flexibility**: Offered in both a 72B-parameter standard model (A.X 4.0) and a 7B lightweight version (A.X 4.0 Light).
 - **Long Context Handling**: Supports up to 131,072 tokens, allowing comprehension of lengthy documents and conversations. (Lightweight model supports up to 16,384 tokens length)
-A brief comparison on representative benchmarks is as follows:
 ## Performance
 ### Model Performance

 - **Deployment Flexibility**: Offered in both a 72B-parameter standard model (A.X 4.0) and a 7B lightweight version (A.X 4.0 Light).
 - **Long Context Handling**: Supports up to 131,072 tokens, allowing comprehension of lengthy documents and conversations. (Lightweight model supports up to 16,384 tokens length)
 ## Performance
 ### Model Performance