Instructions to use naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B with Transformers:

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

pipe = pipeline("text-generation", model="naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B 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-Vision-Instruct-3B"
# 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-Vision-Instruct-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B

SGLang

How to use naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B 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-Vision-Instruct-3B" \
    --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-Vision-Instruct-3B",
		"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-Vision-Instruct-3B" \
        --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-Vision-Instruct-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B with Docker Model Runner:
```
docker model run hf.co/naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B
```

DongHyunKim commited on Sep 16, 2025

Commit

e3dd578

verified ·

1 Parent(s): f0e805a

Update README.md

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@@ -292,7 +292,8 @@ To speed up your inference, you can use the vLLM engine from [our repository](ht
 Make sure to switch to the `v0.9.2rc2_hyperclovax_vision_seed` branch.
 **Launch API server**:
-```
 pyenv virtualenv 3.10.2 .vllm
 pyenv activate .vllm
 sudo apt-get install -y kmod
@@ -317,7 +318,7 @@ pip install -U pynvml
 pip install timm av decord
 # Then launch api
-MODEL="/mnt/cmlssd004/public/donghyun/HCX_models/hcx-instruct/HyperCLOVAX-Seed-Vision-3B_250610"
 export ATTENTION_BACKEND=FLASH_ATTN_VLLM_V1
 VLLM_USE_V1=1 VLLM_ATTENTION_BACKEND=${ATTENTION_BACKEND} CUDA_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.openai.api_server \
     --seed 20250525 \

 Make sure to switch to the `v0.9.2rc2_hyperclovax_vision_seed` branch.
 **Launch API server**:
+```bash
 pyenv virtualenv 3.10.2 .vllm
 pyenv activate .vllm
 sudo apt-get install -y kmod
 pip install timm av decord
 # Then launch api
+MODEL=your/mode/path
 export ATTENTION_BACKEND=FLASH_ATTN_VLLM_V1
 VLLM_USE_V1=1 VLLM_ATTENTION_BACKEND=${ATTENTION_BACKEND} CUDA_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.openai.api_server \
     --seed 20250525 \