Instructions to use naver-hyperclovax/HyperCLOVAX-SEED-Think-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-32B with Transformers:

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

pipe = pipeline("text-generation", model="naver-hyperclovax/HyperCLOVAX-SEED-Think-32B", 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-Think-32B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-32B 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-32B"
# 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-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/naver-hyperclovax/HyperCLOVAX-SEED-Think-32B

SGLang

How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-32B 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-32B" \
    --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-32B",
		"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-32B" \
        --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-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-32B with Docker Model Runner:
```
docker model run hf.co/naver-hyperclovax/HyperCLOVAX-SEED-Think-32B
```

HyperCLOVAX-SEED-Think-32B / processor_config.json

bigshanedogg

Rename processor classes to HyperCLOVAXVisionV2*

c685e77 verified 3 days ago

raw

history blame

2.46 kB

	{
	"processor_class": "HyperCLOVAXVisionV2Processor",
	"image_processor": {
	"discrete_image_end_token": "<\|discrete_image_end\|>",
	"discrete_image_ratios": [
	[
	1,
	1
	],
	[
	1,
	2
	],
	[
	2,
	1
	],
	[
	3,
	4
	],
	[
	4,
	3
	],
	[
	9,
	16
	],
	[
	16,
	9
	],
	[
	1,
	3
	],
	[
	3,
	1
	]
	],
	"discrete_image_start_token": "<\|discrete_image_start\|>",
	"discrete_image_token": "<\|DISCRETE_IMAGE_PAD\|>",
	"discrete_token_size": 27,
	"resize_mode": "smart_resize",
	"do_center_crop": false,
	"crop_size": null,
	"do_convert_rgb": true,
	"do_normalize": true,
	"do_rescale": true,
	"do_resize": true,
	"image_end_token": "<\|image_end\|>",
	"image_mean": [
	0.48145466,
	0.4578275,
	0.40821073
	],
	"image_processor_type": "HyperCLOVAXVisionV2ImageProcessor",
	"image_start_token": "<\|image_start\|>",
	"image_std": [
	0.26862954,
	0.26130258,
	0.27577711
	],
	"image_token": "<\|IMAGE_PAD\|>",
	"max_pixels": 2073600,
	"merge_size": 2,
	"min_pixels": 3136,
	"patch_size": 14,
	"resample": 3,
	"rescale_factor": 0.00392156862745098,
	"size": {
	"longest_edge": 2073600,
	"shortest_edge": 3136
	},
	"temporal_patch_size": 2,
	"use_discrete_token": false,
	"vision_eof_token": "<\|vision_eof\|>",
	"vision_eol_token": "<\|vision_eol\|>"
	},
	"video_processor": {
	"do_convert_rgb": true,
	"do_normalize": true,
	"do_rescale": true,
	"do_resize": true,
	"image_mean": [
	0.48145466,
	0.4578275,
	0.40821073
	],
	"image_std": [
	0.26862954,
	0.26130258,
	0.27577711
	],
	"max_frames": 120,
	"max_pixels": 285768,
	"merge_size": 2,
	"min_pixels": 3136,
	"patch_size": 14,
	"resample": 3,
	"rescale_factor": 0.00392156862745098,
	"temporal_patch_size": 2,
	"use_audio_in_video": false,
	"use_discrete_token": false,
	"video_end_token": "<\|video_end\|>",
	"video_processor_type": "HyperCLOVAXVisionV2VideoProcessor",
	"video_start_token": "<\|video_start\|>",
	"video_token": "<\|VIDEO_PAD\|>",
	"video_audio_token": "<\|VIDEO_AUDIO_PAD\|>",
	"vision_eof_token": "<\|vision_eof\|>",
	"vision_eol_token": "<\|vision_eol\|>"
	}
	}