HyperCLOVAX-SEED-Text-Instruct-0.5B-hf

Instructions to use minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf")
model = AutoModelForCausalLM.from_pretrained("minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf

SGLang

How to use minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf with Docker Model Runner:
```
docker model run hf.co/minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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))

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Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for minpeter/HyperCLOVAX-SEED-Text-Instruct-0.5B-hf

Base model

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

Finetuned

(7)

this model

Quantizations

2 models