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

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-32B 모델의 `model_type`을 `hyperclovax_vision_v2`으로 변경 요청드립니다 (transformers PR 연계)

#11

by jp1924 - opened Mar 20

base: refs/heads/main

←

from: refs/pr/11

Discussion Files changed

-2

Files changed (2) hide show

config.json +1 -1
configuration_vlm.py +1 -1

config.json CHANGED Viewed

@@ -22,7 +22,7 @@
   "is_safetensor_save": true,
   "max_num_grids": -1,
   "mm_projector_type": "linear",
-  "model_type": "vlm",
   "num_queries_vis_abstractor": -1,
   "pad_token_id": 0,
   "possible_resolutions": [],

   "is_safetensor_save": true,
   "max_num_grids": -1,
   "mm_projector_type": "linear",
+  "model_type": "hyperclovax_vision_v2",
   "num_queries_vis_abstractor": -1,
   "pad_token_id": 0,
   "possible_resolutions": [],

configuration_vlm.py CHANGED Viewed

@@ -3,7 +3,7 @@ from transformers import AutoConfig, PretrainedConfig
 class HCXVisionConfig(PretrainedConfig):
-    model_type = "vlm"
     keys_to_ignore_at_inference = ["past_key_values"]
     def __init__(

 class HCXVisionConfig(PretrainedConfig):
+    model_type = "hyperclovax_vision_v2"
     keys_to_ignore_at_inference = ["past_key_values"]
     def __init__(