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

Libraries

How to use naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B")
model = AutoModelForCausalLM.from_pretrained("naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B")
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

vLLM

How to use naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

How to use naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B 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 "naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B" \
    --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": "naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B",
		"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 "naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B" \
        --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": "naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B with Docker Model Runner:
```
docker model run hf.co/naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B
```

Missing generation_config.json breaks GenerationConfig.from_pretrained()

by Bias92 - opened Feb 7

Discussion

Bias92

Feb 7

•

edited Feb 7

Summary

generation_config.json is not included in this repository, which causes
GenerationConfig.from_pretrained() to fail with a file-not-found error.
While model.generate() still works via fallback from the model config (model.config),
this breaks explicit generation config loading workflows and some serving frameworks.

Reproduction

from transformers import GenerationConfig
config = GenerationConfig.from_pretrained(
    "naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B"
)
# Error: does not appear to have a file named generation_config.json

Environment

transformers: latest (installed from main branch)
torch: 2.10.0
Platform: macOS, Apple M4 Pro (MPS)

Impact

GenerationConfig.from_pretrained() raises an error
Workflows relying on explicit GenerationConfig.from_pretrained() load/save cycle break
Some serving stacks (e.g., DeepSpeed-MII) may assume generation_config.json exists
Inconsistent with other models of similar scale (Qwen2.5-0.5B, Llama-3.2-1B, EXAONE-3.5-2.4B all include it)

Token IDs from tokenizer (for reference)

bos_token_id: 100257  (<|endoftext|>)
eos_token_id: 100275  (<|endofturn|>)
pad_token_id: 100257  (<|endoftext|>)

Suggestion

Would it be possible to add a minimal generation_config.json?
A minimal version would be:

{
  "_from_model_config": true,
  "bos_token_id": 100257,
  "eos_token_id": 100275,
  "pad_token_id": 100257
}

I noticed that the Vision-Instruct-3B repo had generation_config.json removed in a recent commit,
so this may be intentional. If so, could you share the reasoning?

Happy to submit a PR if this would be welcome.

Notes

model.generate() works fine via model config fallback — this is not a model inference bug
This applies to the Text-Instruct series; I was unable to verify 1.5B due to gated access

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