Instructions to use naver-hyperclovax/HyperCLOVAX-SEED-Think-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| {%- set ns_img = namespace(count=0) %} | |
| {%- set ns_vid = namespace(count=0) %} | |
| {%- if tools %} | |
| {{- '<|im_start|>system\n' }} | |
| {%- if messages[0].role == 'system' and messages[0].content is string %} | |
| {{- messages[0].content + '\n\n' }} | |
| {%- endif %} | |
| {{- '# Tools\n\n' }} | |
| {{- 'You may call one or more functions to assist with the user query.\n\n' }} | |
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| {{- 'For each function call, output the function name and arguments within the following XML format:\n' }} | |
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| {{- '<arg_key>{arg-key-1}</arg_key>\n' }} | |
| {{- '<arg_value>{arg-value-1}</arg_value>\n' }} | |
| {{- '<arg_key>{arg-key-2}</arg_key>\n' }} | |
| {{- '<arg_value>{arg-value-2}</arg_value>\n' }} | |
| {{- '...\n' }} | |
| {{- '</tool_call><|im_end|>\n' }} | |
| {%- else %} | |
| {%- if messages[0].role == 'system' %} | |
| {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- set ns = namespace(last_user_index=-1) %} | |
| {%- for m in messages %} | |
| {%- if m.role == 'user' %} | |
| {%- set ns.last_user_index = loop.index0 %} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- for message in messages %} | |
| {%- set content = message.get('content', '') or '' %} | |
| {%- if (message.role == 'system' and not loop.first) %} | |
| {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>\n' }} | |
| {%- elif message.role == 'user' %} | |
| {{- '<|im_start|>user\n' }} | |
| {%- if message['content'] is string %} | |
| {{- message['content'] + '<|im_end|>\n' }} | |
| {%- elif message['content'] is sequence %} | |
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| {{- '\n' }} | |
| {%- endif %} | |
| {%- if content['type'] == 'image_url' %} | |
| {%- set media_url = content.get('image_url', {}).get('url', '') %} | |
| {%- set url_lower = media_url.lower() %} | |
| {%- set video_extensions = ['.mp4', '.avi', '.mov', '.mkv', '.webm', '.flv', '.wmv', '.m4v'] %} | |
| {%- set ns_check = namespace(is_video=False) %} | |
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| {%- set video_id = 'video_%02d' % ns_vid.count %} | |
| {%- set ns_vid.count = ns_vid.count + 1 %} | |
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| {%- else %} | |
| {%- set image_id = 'image_%02d' % ns_img.count %} | |
| {%- set ns_img.count = ns_img.count + 1 %} | |
| {{- '<|mime_start|>{"id": "' + image_id + '", "type": "image/jpeg", "filename": "image.jpg"}<|mime_end|>\n' }} | |
| {{- '<|image_start|><|IMAGE_PAD|><|image_end|>' }} | |
| {%- endif %} | |
| {%- elif content['type'] == 'text' %} | |
| {{- content['text'] }} | |
| {%- endif %} | |
| {%- endfor %} | |
| {{- '<|im_end|>\n'}} | |
| {%- endif %} | |
| {%- elif message.role == 'assistant' %} | |
| {%- set reasoning_content = '' %} | |
| {%- if message.get('reasoning_content') is string %} | |
| {%- set reasoning_content = message.get('reasoning_content') %} | |
| {%- else %} | |
| {%- if '</think>' in content %} | |
| {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %} | |
| {%- set content = content.split('</think>')[-1].lstrip('\n') %} | |
| {%- endif %} | |
| {%- endif %} | |
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| {%- if loop.last or reasoning_content %} | |
| {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }} | |
| {%- else %} | |
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| {%- endif %} | |
| {%- else %} | |
| {{- '<|im_start|>' + message.role + '\n' + content }} | |
| {%- endif %} | |
| {%- if message.get('tool_calls') %} | |
| {%- for tool_call in message.get('tool_calls', []) %} | |
| {%- if not loop.first or content %} | |
| {{- '\n' }} | |
| {%- endif %} | |
| {%- if tool_call.get('function') %} | |
| {%- set tool_call = tool_call.get('function') %} | |
| {%- endif %} | |
| {{- '<tool_call>' + tool_call.get('name', '') + '\n' }} | |
| {%- set _args = tool_call.get('arguments', {}) %} | |
| {%- if _args is string %} | |
| {{- '<arguments>' + _args + '</arguments>\n' }} | |
| {%- elif _args is mapping %} | |
| {%- for k, v in _args.items() %} | |
| {{- '<arg_key>' + k + '</arg_key>\n' }} | |
| {{- '<arg_value>' + (v | tojson(ensure_ascii=False) if v is not string else v) + '</arg_value>\n' }} | |
| {%- endfor %} | |
| {%- endif %} | |
| {{- '</tool_call>' }} | |
| {%- endfor %} | |
| {%- endif %} | |
| {{- '<|im_end|>\n' }} | |
| {%- elif message.role == 'tool' %} | |
| {%- if loop.first or (messages[loop.index0 - 1].role != 'tool') %} | |
| {{- '<|im_start|>tool' }} | |
| {%- endif %} | |
| {{- '\n<tool_response>' + message.get('name', '') + '\n' }} | |
| {{- content }} | |
| {{- '\n</tool_response>' }} | |
| {%- if loop.last or (messages[loop.index0 + 1].role != 'tool') %} | |
| {{- '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- if add_generation_prompt %} | |
| {%- if thinking is defined and thinking is true %} | |
| {{- '<|im_start|>assistant\n<think>\n' }} | |
| {%- else %} | |
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| {%- endif %} | |
| {%- endif %} | |