Instructions to use naver-hyperclovax/HyperCLOVAX-SEED-Think-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-14B with Transformers:

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

pipe = pipeline("text-generation", model="naver-hyperclovax/HyperCLOVAX-SEED-Think-14B", trust_remote_code=True)
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-Think-14B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("naver-hyperclovax/HyperCLOVAX-SEED-Think-14B", trust_remote_code=True)
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 naver-hyperclovax/HyperCLOVAX-SEED-Think-14B 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-Think-14B"
# 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-Think-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/naver-hyperclovax/HyperCLOVAX-SEED-Think-14B

SGLang

How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-14B 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-Think-14B" \
    --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-Think-14B",
		"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-Think-14B" \
        --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-Think-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-14B with Docker Model Runner:
```
docker model run hf.co/naver-hyperclovax/HyperCLOVAX-SEED-Think-14B
```

[Notice] Migration to Native transformers Support (>= 5.9.0)

#11

by bigshanedogg - opened 6 days ago

Discussion

bigshanedogg

6 days ago

We would like to respectfully share an important update regarding HyperCLOVAX-SEED-Think-14B.

The model is now natively supported in the Hugging Face transformers library as of version 5.9.0, following the merge of huggingface/transformers#44956. The model architecture, configuration, and tokenizer are now part of the official transformers distribution, and loading the model no longer requires the trust_remote_code=True flag.

What This Means

Recommended environment: transformers >= 5.9.0. We have verified that the checkpoint files in this repository work correctly with this version using the standard AutoModelForCausalLM.from_pretrained(...) and AutoTokenizer.from_pretrained(...) calls — no additional flags are required.
Older transformers versions: While the legacy remote-code path (via auto_map) may continue to work in some cases, its behavior is no longer guaranteed and you may encounter unexpected issues. We therefore kindly recommend upgrading to the latest release.
README updates: All code examples have been refreshed to remove trust_remote_code=True and to reflect the new minimum version. Please refer to the latest README for the most up-to-date usage examples.

Upgrade Command

pip install -U "transformers>=5.9.0"

Feedback

Should you encounter any unexpected behavior — particularly around model loading, generation, chat template handling, or tokenization — we would be very grateful if you could open a Discussion thread on this repository. We will do our best to investigate and respond promptly.

We sincerely thank you for your continued interest in and support of HyperCLOVA X SEED.
Your engagement and thoughtful feedback are invaluable to us, and we deeply appreciate the community's role in helping us improve the model and its surrounding tooling.

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