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*This model was contributed to Hugging Face Transformers on 2026-07-24.*

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    <div class="flex flex-wrap space-x-1">
        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
    </div>
</div>

# A.X-K2

[A.X-K2](https://huggingface.co/skt) is SK Telecom's flagship large language model. It is a
Mixture-of-Experts decoder built on the DeepSeek-V3.2 architecture — Multi-head Latent Attention (MLA)
with DeepSeek Sparse Attention (DSA) — plus three SK Telecom modifications:

- **Sparse Gated Attention (SGA)**: every layer runs a lightweight *lightning indexer* that scores each
  query against the keys and keeps only the top-`index_topk` positions, which become an additive sparse
  mask folded into the MLA attention. The indexer maintains its own key cache alongside the main KV
  cache (`DynamicIndexedLayer` / `StaticIndexedLayer`).
- **Gated RMSNorm**: `input_layernorm` (every layer) and `post_attention_layernorm` (MoE layers) are
  wrapped with a low-rank input-dependent sigmoid gate, `RMSNorm(x) * sigmoid(gate_mlp(RMSNorm(x)))`.
- **Attention output gate**: the attention output is multiplied by an input-dependent sigmoid gate
  (`g_proj`) before the output projection. In the released checkpoint this gate is fused into `q_b_proj`
  (vLLM layout) and split back out at load time by the weight converter.

Routing is plain (non-grouped) sigmoid top-k with a correction bias; the first layer is dense and the
rest are MoE (with a shared expert).

> [!TIP]
> A.X-K2 relies on an explicit additive sparse mask, so it runs under the `eager` and `sdpa` attention
> implementations (`attn_implementation="sdpa"` is the default and recommended backend).

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-K2")

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-K2")
model = AutoModelForCausalLM.from_pretrained("skt/A.X-K2", 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>

## AXK2Config

[[autodoc]] AXK2Config

## AXK2Model

[[autodoc]] AXK2Model
    - forward

## AXK2ForCausalLM

[[autodoc]] AXK2ForCausalLM
    - forward

## AXK2ForSequenceClassification

[[autodoc]] AXK2ForSequenceClassification
    - forward

## AXK2ForTokenClassification

[[autodoc]] AXK2ForTokenClassification
    - forward
