Instructions to use ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive") model = AutoModelForCausalLM.from_pretrained("ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive
- SGLang
How to use ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive 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 "ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive" \ --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": "ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive", "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 "ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive" \ --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": "ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive with Docker Model Runner:
docker model run hf.co/ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive
- Xet hash:
- bf8f792545e1d42729d1bd676449c3a3bda6ab38f21683ca3f1de678f4521dac
- Size of remote file:
- 1.33 kB
- SHA256:
- 21de9f3363778eb3b00f4893145ee6f8c0dbdd039be0d6d8c77341a0853e0205
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