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
- Transformers
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)# pip install -U transformers accelerate # 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
Commit History
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change double for loop to list comprehension for upgrading speed
Verified signature```python import timeit class DummyConfig: def __init__(self, max_num_grids=10, use_1x1_grid=False, anyres=True): self.max_num_grids = max_num_grids self.use_1x1_grid = use_1x1_grid self.anyres = anyres config = DummyConfig() def original_version(): possible_resolutions = [] if config.anyres: assert config.max_num_grids > 0 for i in range(1, config.max_num_grids + 1): for j in range(1, config.max_num_grids + 1): if i == 1 and j == 1 and not config.use_1x1_grid: continue if i * j <= config.max_num_grids: possible_resolutions.append([i, j]) return possible_resolutions def list_comprehension_version(): possible_resolutions = [] if config.anyres: assert config.max_num_grids > 0 possible_resolutions = [ [i, j] for i in range(1, config.max_num_grids + 1) for j in range(1, (config.max_num_gri…4f02e63
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Update modeling_hyperclovax.py
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Update modeling_hyperclovax.py
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Update modeling_hyperclovax.py
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update readme. better user experience
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HyperCLOVAX-Seed-Vision-3B
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