naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B

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

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))