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*This model was contributed to Hugging Face Transformers on 2026-07-23.*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
    </div>
</div>

# A.X-K1

[A.X-K1](https://huggingface.co/skt) is SK Telecom's Mixture-of-Experts large language model. It is
built on the DeepSeek-V3 architecture — Multi-head Latent Attention (MLA) with a grouped sigmoid
top-k MoE and a shared expert — with one SK Telecom modification: an extra **`post_mlp_layernorm`**
applied to the MoE block output before the residual add. The first layer is dense and the rest are
MoE.

Because attention is standard (dense) MLA, A.X-K1 runs under all attention backends (FlashAttention-2,
SDPA, and eager).

The example below shows how to generate text with [`Pipeline`] or the [`AutoModel`].

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from transformers import pipeline

pipe = pipeline(
    task="text-generation",
    model="skt/A.X-K1",
)

print(pipe("대한민국의 수도는", max_new_tokens=32)[0]["generated_text"])
```

</hfoption>
<hfoption id="AutoModel">

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("skt/A.X-K1")
model = AutoModelForCausalLM.from_pretrained(
    "skt/A.X-K1",
    device_map="auto",
)

inputs = tokenizer("대한민국의 수도는", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=32, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

</hfoption>
</hfoptions>

## AXK1Config

[[autodoc]] AXK1Config

## AXK1Model

[[autodoc]] AXK1Model
    - forward

## AXK1ForCausalLM

[[autodoc]] AXK1ForCausalLM
    - forward

## AXK1ForSequenceClassification

[[autodoc]] AXK1ForSequenceClassification
    - forward

## AXK1ForTokenClassification

[[autodoc]] AXK1ForTokenClassification
    - forward
