Instructions to use chen-hao-chao/mdm-prime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chen-hao-chao/mdm-prime with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="chen-hao-chao/mdm-prime")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chen-hao-chao/mdm-prime", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use chen-hao-chao/mdm-prime with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "chen-hao-chao/mdm-prime" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chen-hao-chao/mdm-prime", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/chen-hao-chao/mdm-prime
- SGLang
How to use chen-hao-chao/mdm-prime 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 "chen-hao-chao/mdm-prime" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chen-hao-chao/mdm-prime", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "chen-hao-chao/mdm-prime" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chen-hao-chao/mdm-prime", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use chen-hao-chao/mdm-prime with Docker Model Runner:
docker model run hf.co/chen-hao-chao/mdm-prime
Upload folder using huggingface_hub
Browse files- image/cifar10/results_mdm_cifar10/checkpoint-2499.pth +3 -0
- image/cifar10/results_prime_l2_cifar10/checkpoint-4249.pth +3 -0
- image/cifar10/results_prime_l3_cifar10/checkpoint-3499.pth +3 -0
- image/cifar10/results_prime_l4_cifar10/checkpoint-2499.pth +3 -0
- image/cifar10/results_prime_l6_cifar10/checkpoint-1499.pth +3 -0
- image/cifar10/results_prime_l8_cifar10/checkpoint-1499.pth +3 -0
- image/cifar10/results_prime_supertoken_cifar10/checkpoint-2249.pth +3 -0
- image/imagenet32/results_mdm_imagenet32/checkpoint-674.pth +3 -0
- image/imagenet32/results_prime_l2_imagenet32/checkpoint-974.pth +3 -0
- image/imagenet32/results_prime_l3_imagenet32/checkpoint-1049.pth +3 -0
- image/imagenet32/results_prime_l4_imagenet32/checkpoint-899.pth +3 -0
- image/imagenet32/results_prime_l6_imagenet32/checkpoint-674.pth +3 -0
- image/imagenet32/results_prime_l8_imagenet32/checkpoint-649.pth +3 -0
- image/imagenet32/results_prime_supertoken_imagenet32/checkpoint-599.pth +3 -0
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