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Sponsored by
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minpeter / HyperCLOVAX-SEED-Text-Instruct-0.5B-hf
Model's Last Updated: April 24 2025
text-generation
Introduction of HyperCLOVAX-SEED-Text-Instruct-0.5B-hf
Model Details of HyperCLOVAX-SEED-Text-Instruct-0.5B-hf
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:
- Pretraining : Knowledge acquisition using high‑quality data and a high‑performance pretrained model.
- Rejection Sampling Fine‑Tuning (RFT) : Enhancement of multi‑domain knowledge and complex reasoning capabilities.
- 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))
Runs of minpeter HyperCLOVAX-SEED-Text-Instruct-0.5B-hf on huggingface.co
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More Information About HyperCLOVAX-SEED-Text-Instruct-0.5B-hf huggingface.co Model
Url of HyperCLOVAX-SEED-Text-Instruct-0.5B-hf
HyperCLOVAX-SEED-Text-Instruct-0.5B-hf huggingface.co Url
Provider of HyperCLOVAX-SEED-Text-Instruct-0.5B-hf huggingface.co
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