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

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
```

Update modeling_hyperclovax.py

by kibitzing - opened Aug 27, 2025

base: refs/heads/main

←

from: refs/pr/6

Discussion Files changed

+11

-3

Files changed (1) hide show

modeling_hyperclovax.py +11 -3

modeling_hyperclovax.py CHANGED Viewed

@@ -1,6 +1,7 @@
 # coding=utf-8
 # This file was created for the HyperCLOVA X SEED 14B Think architecture.
-# partially copied and modified from https://github.com/huggingface/transformers
 # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
 #
 # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
@@ -43,7 +44,14 @@ from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_u
 from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from transformers.processing_utils import Unpack
 from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
-from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
 from .configuration_hyperclovax import HyperCLOVAXConfig
 if is_torch_flex_attn_available():
     from torch.nn.attention.flex_attention import BlockMask
@@ -620,7 +628,7 @@ class HyperCLOVAXModel(HyperCLOVAXPreTrainedModel):
         return causal_mask
-class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
 @auto_docstring

 # coding=utf-8
 # This file was created for the HyperCLOVA X SEED 14B Think architecture.
+# partially copied and modified from
+# https://github.com/huggingface/transformers/blob/v4.52.4/src/transformers/models/llama/modeling_llama.py
 # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
 #
 # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
 from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from transformers.processing_utils import Unpack
 from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
+from transformers.utils import auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
+try:
+    from transformers.utils import LossKwargs
+    loss_kwargs_class = LossKwargs
+except ImportError:
+    from transformers.utils import TransformersKwargs
+    loss_kwargs_class = TransformersKwargs
 from .configuration_hyperclovax import HyperCLOVAXConfig
 if is_torch_flex_attn_available():
     from torch.nn.attention.flex_attention import BlockMask
         return causal_mask
+class KwargsForCausalLM(FlashAttentionKwargs, loss_kwargs_class): ...
 @auto_docstring