Model reference · open weights
HyperCLOVAX-SEED-Think is an open-weight language model from naver-hyperclovax. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | naver-hyperclovax |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 33.3B |
| Context | 128k tokens |
| Runs with | transformers |
| Released | 2025-12-23 |
| Popularity | 13k downloads / month |
| Licence | Commercial licence needed |
About
HyperCLOVA X SEED 32B Think is an updated vision-language thinking model that advances the SEED Think 14B line beyond simple scaling, pairing a unified vision-language Transformer backbone with a reasoning-centric training recipe. SEED 32B Think processes text tokens and visual patches within a shared embedding space, supports long-context multimodal understanding up to 128K tokens, and provides an optional “thinking mode” for deep, controllable reasoning. Building on the earlier 14B model, SEED 32B Think further strengthens Korean-centric reasoning and agentic capabilities, improving practical reasoning quality and reliability in real-world use.
We provide OmniServe, a production-ready multimodal inference system with OpenAI-compatible API.
# Clone OmniServe
git clone https://github.com/NAVER-Cloud-HyperCLOVA-X/OmniServe.git
cd OmniServe
# Install dependencies
pip install huggingface_hub safetensors torch openai easydict
# Download model (~60GB)
huggingface-cli download naver-hyperclovax/HyperCLOVAX-SEED-Think-32B \
--local-dir ./models/HyperCLOVAX-SEED-Think-32B
# Convert model to component format
python convert_model.py \
--input ./models/HyperCLOVAX-SEED-Think-32B \
--output ./track_a \
--track a
# Configure environment
cp .env.example .env
# Edit .env:
# VLM_MODEL_PATH=./track_a/llm/HyperCLOVAX-SEED-Think-32B
# VLM_ENCODER_VISION_MODEL_PATH=./track_a/ve/HyperCLOVAX-SEED-Think-32B
# Build and run
docker compose --profile track-a build
docker compose --profile track-a up -d
# Wait for model loading (~5 minutes)
docker compose logs -f vlm
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8000/a/v1",
api_key="not-needed"
)
# Image understanding
response = client.chat.completions.create(
model="track_a_model",
messages=[
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}},
{"type": "text", "text": "Describe this image."}
]
}
],
max_tokens=512,
extra_body={"chat_template_kwargs": {"thinking": False}}
)
print(response.choices[0].message.content)
Enable chain-of-thought reasoning for complex tasks:
response = client.chat.completions.create(
model="track_a_model",
messages=[
{"role": "user", "content": "Solve step by step: 3x + 7 = 22"}
],
max_tokens=1024,
extra_body={
"thinking_token_budget": 500,
"chat_template_kwargs": {"thinking": True}
}
)
# Response includes ... with reasoning process
print(response.choices[0].message.content)
response = client.chat.completions.create(
model="track_a_model",
messages=[
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "https://example.com/video.mp4"}},
{"type": "text", "text": "Describe this video."}
]
}
],
max_tokens=512,
extra_body={"chat_template_kwargs": {"thinking": False}}
)
import base64
with open("image.png", "rb") as f:
image_b64 = base64.b64encode(f.read()).decode()
response = client.chat.completions.create(
model="track_a_model",
messages=[
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_b64}"}},
{"type": "text", "text": "What is in this image?"}
]
}
],
max_tokens=512,
extra_body={"chat_template_kwargs": {"thinking": False}}
)
curl -X POST http://localhost:8000/a/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "track_a_model",
"messages": [
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}},
{"type": "text", "text": "Describe this image."}
]
}
],
"max_tokens": 512,
"extra_body": {"chat_template_kwargs": {"thinking": false}}
}'
| Input | Output |
|---|---|
| Text | Text |
| Image | Text |
| Video | Text |
| Image + Text | Text |
| Video + Text | Text |
Features:
... output User Request
(Image/Video/Text)
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ OmniServe │
│ POST /a/v1/chat/completions │
│ │
│ ┌────────────────────────────────────────────────From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys hyperclovax-seed-think for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (hyperclovax-seed-think below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"hyperclovax-seed-think","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.