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<<<<<<< Updated upstream
*This model was released on 2025-07-21 and added to Hugging Face Transformers on 2026-05-08.*
=======
*This model was contributed to Hugging Face Transformers on 2026-05-08.*
>>>>>>> Stashed changes

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# HyperCLOVA X

## Overview

HyperCLOVA X SEED Think is NAVER Cloud's language model combining pruning and knowledge distillation with advanced reasoning capabilities. The 14B model features a Transformer-based architecture with Peri-Layer Normalization and Maximal Update Parameterization (μP), 14.74B parameters, and 32k context length. It supports dual-mode reasoning (think / non-think) and function calling via a ChatML-based format.

The model was trained with a multi-stage RL pipeline (SFT → RLVR → Length Controllability → joint RLHF+RLVR) and achieves strong performance on Korean language benchmarks and reasoning tasks.

HyperCLOVA X shares a high degree of implementation similarity with [Granite](./granite), with the following modifications:

- **Maximal Update Parametrization (MuP)**: uses per-config scaling factors (`attention_multiplier`, `residual_multiplier`, `embedding_multiplier`, `logits_scaling`) to enable stable training across model sizes. `head_dim` (defaults to `hidden_size // num_attention_heads`) is used to compute the default `attention_multiplier`.
- **Peri-Layer Normalization** (optional): applies an extra RMSNorm after each sub-layer output when `use_post_norm=True`.

This model was contributed by [NAVER Cloud HyperCLOVA X Team](https://huggingface.co/naver-hyperclovax). The original model can be found at [naver-hyperclovax/HyperCLOVAX-SEED-Think-14B](https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Think-14B).

## Usage

The model uses a ChatML-based format with special tokens `<|im_start|>`, `<|im_end|>`, `<|endofturn|>`, and `<|stop|>`. The `apply_chat_template` method accepts the following kwargs:

- `force_reasoning=True` — always think before answering
- `skip_reasoning=True` — always answer directly (non-think mode)
- Default (`None`) — model decides based on context

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

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "naver-hyperclovax/HyperCLOVAX-SEED-Think-14B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of South Korea?"},
]
# Pass force_reasoning=True to always think, or skip_reasoning=True to skip thinking.
model_inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    # force_reasoning=True,
    # skip_reasoning=True,
).to(model.device)

output = model.generate(
    **model_inputs,
    tokenizer=tokenizer,
)
print(tokenizer.decode(output[0][model_inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
```

</hfoption>
</hfoptions>

## HyperCLOVAXConfig

[[autodoc]] HyperCLOVAXConfig

## HyperCLOVAXModel

[[autodoc]] HyperCLOVAXModel
    - forward

## HyperCLOVAXForCausalLM

[[autodoc]] HyperCLOVAXForCausalLM
    - forward