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
```

jeongin601 commited on Jul 21, 2025

Commit

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@@ -220,7 +220,7 @@ Tell me the weather in Seoul<|im_end|>
 ```
-- Note that the prompt ends with `assistant/think\n`(think + \n).
 - Generation continues until either the <|stop|> or <|endofturn|> token appears immediately after `<|im_end|>`.
 To have the assistant respond in non-reasoning mode (i.e., answer directly), you can input the following prompt.
@@ -232,7 +232,7 @@ Tell me the weather in Seoul<|im_end|>
 ```
-- Note that the prompt ends with `assistant\n`.
 - Generation continues until either the <|stop|> or <|endofturn|> token appears immediately after `<|im_end|>`.
@@ -545,7 +545,7 @@ print(tokenizer.batch_decode(output_ids))
 ## **vLLM Usage Example**
-he HyperCLOVA X SEED Think model is built on a custom LLM architecture based on the LLaMA architecture, incorporating μP and Peri-LN techniques. For convenient use with vLLM, it is available as a dedicated vLLM plugin that can be installed and used with ease once vLLM is set up.
 1. Download vLLM plugin source code

 ```
+- Note that the prompt ends with `assistant/think\n`(think + `\n`).
 - Generation continues until either the <|stop|> or <|endofturn|> token appears immediately after `<|im_end|>`.
 To have the assistant respond in non-reasoning mode (i.e., answer directly), you can input the following prompt.
 ```
+- Note that the prompt ends with `assistant\n`(assistant + `\n`).
 - Generation continues until either the <|stop|> or <|endofturn|> token appears immediately after `<|im_end|>`.
 ## **vLLM Usage Example**
+The HyperCLOVA X SEED Think model is built on a custom LLM architecture based on the LLaMA architecture, incorporating μP and Peri-LN techniques. For convenient use with vLLM, it is available as a dedicated vLLM plugin that can be installed and used with ease once vLLM is set up.
 1. Download vLLM plugin source code