---
license: other
license_name: hyperclovax-seed
license_link: LICENSE
base_model:
- naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B
pipeline_tag: text-generation
library_name: transformers
---


![image/png](https://cdn-uploads.huggingface.co/production/uploads/65265ab8f8db96cffcb969dc/szGAraJ_ZawG0kozH5yPi.png)


## Overview

HyperCLOVAX-SEED-Text-Instruct-0.5B is a Text-to-Text model with instruction-following capabilities that excels in understanding Korean language and culture. Compared to external competitors of similar scale, it demonstrates improved mathematical performance and a substantial enhancement in Korean language capability. The HyperCLOVAX-SEED-Text-Instruct-0.5B is currently the smallest model released by the HyperCLOVAX, representing a lightweight solution suitable for deployment in resource‑constrained environments such as edge devices. It supports a maximum context length of 4K and functions as a versatile small model applicable to a wide range of tasks. The total cost of a single training run for HyperCLOVAX-SEED-Text-Instruct-0.5B was 4.358K A100 GPU hours (approximately USD 6.537K), which is 39 times lower than the cost of training the `QWEN2.5‑0.5B‑instruct` model.


## Basic Information

- **Architecture**: Transformer‑based (Dense Model)  
- **Parameters**: 0.57 B (total); 0.45 B (excluding token embeddings, tied embeddings)  
- **Input/Output Format**: Text / Text  
- **Maximum Context Length**: 4 K tokens  
- **Knowledge Cutoff Date**: Trained on data up to January 2025


## Training and Data

The training dataset for HyperCLOVAX-SEED-Text-Instruct-0.5B consists of diverse sources, including the high‑quality data accumulated during the development of HyperCLOVAX-SEED-Text-Instruct-0.5B. Training was conducted in three main stages:
1. **Pretraining**: Knowledge acquisition using high‑quality data and a high‑performance pretrained model.  
2. **Rejection Sampling Fine‑Tuning (RFT)**: Enhancement of multi‑domain knowledge and complex reasoning capabilities.  
3. **Supervised Fine‑Tuning (SFT)**: Improvement of instruction‑following proficiency.


## Training Cost

HyperCLOVAX-SEED-Text-Instruct-0.5B leveraged HyperCLOVA X’s lightweight training process and high‑quality data to achieve significantly lower training costs compared to industry‑leading competitors of similar scale. Excluding the SFT stage, a single pretraining run incurred:  

| Pretraining Cost Category       | HyperCLOVAX-SEED-Text-Instruct-0.5B  | QWEN2.5‑0.5B‑instruct  |
|---------------------------------|-----------------------------------------------|-------------------------------------|
| **A100 GPU Hours**              | 4.358 K                                       | 169.257 K                           |
| **Cost (USD)**                  | 6.537 K                                       | 253.886 K                           |

This represents approximately a 39× reduction in pretraining cost relative to `QWEN2.5‑0.5B-instruct`.

## Benchmarks

| **Model** | **KMMLU (5-shot, acc)** | **HAE-RAE (5-shot, acc)** | **CLiCK (5-shot, acc)** | **KoBEST (5-shot, acc)** |
| --- | --- | --- | --- | --- | 
| HyperCLOVAX-SEED-Text-Base-0.5B | 0.4181 | 0.6370 | 0.5373 | 0.6963
| HyperCLOVAX-SEED-Text-Instruct-0.5B | 0.3815 | 0.5619 | 0.4446 | 0.6299 | 
| QWEN2.5-0.5B-instruct        | 0.2968 | 0.3428 | 0.3805 | 0.5025 | 

## HuggingFace Usage Example

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B")
tokenizer = AutoTokenizer.from_pretrained("naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B")

chat = [
  {"role": "tool_list", "content": ""},
  {"role": "system", "content": "- AI 언어모델의 이름은 \"CLOVA X\" 이며 네이버에서 만들었다.\n- 오늘은 2025년 04월 24일(목)이다."},
  {"role": "user", "content": "슈뢰딩거 방정식과 양자역학의 관계를 최대한 자세히 알려줘."},
]

inputs = tokenizer.apply_chat_template(chat, add_generation_prompt=True, return_dict=True, return_tensors="pt")
output_ids = model.generate(**inputs, max_length=1024, stop_strings=["<|endofturn|>", "<|stop|>"], tokenizer=tokenizer)
print(tokenizer.batch_decode(output_ids))
```