Instructions to use asad/DialoGPT-small-harryporter_bot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asad/DialoGPT-small-harryporter_bot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="asad/DialoGPT-small-harryporter_bot")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("asad/DialoGPT-small-harryporter_bot") model = AutoModelForCausalLM.from_pretrained("asad/DialoGPT-small-harryporter_bot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use asad/DialoGPT-small-harryporter_bot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "asad/DialoGPT-small-harryporter_bot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "asad/DialoGPT-small-harryporter_bot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/asad/DialoGPT-small-harryporter_bot
- SGLang
How to use asad/DialoGPT-small-harryporter_bot 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 "asad/DialoGPT-small-harryporter_bot" \ --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": "asad/DialoGPT-small-harryporter_bot", "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 "asad/DialoGPT-small-harryporter_bot" \ --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": "asad/DialoGPT-small-harryporter_bot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use asad/DialoGPT-small-harryporter_bot with Docker Model Runner:
docker model run hf.co/asad/DialoGPT-small-harryporter_bot
Download pytorch_model.bin from asad/DialoGPT-small-harryporter_bot: direct link, hf CLI and curl.
- Browser
- Download file 510 MB
-
https://huggingface.co/asad/DialoGPT-small-harryporter_bot/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://asad/DialoGPT-small-harryporter_bot/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/asad/DialoGPT-small-harryporter_bot/resolve/main/pytorch_model.bin
510 MB
- Xet hash:
- 3ff74cc6e4bbc14a472a96658a0c27942840a2428fe5073d26079121eb33bebf
- Size of remote file:
- 510 MB
- SHA256:
- 745712b62e867aa69c6431f5d72a690d58663f2a3067878be1d2287b38467403
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