Instructions to use edbeeching/gpt2-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edbeeching/gpt2-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="edbeeching/gpt2-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("edbeeching/gpt2-imdb") model = AutoModelForCausalLM.from_pretrained("edbeeching/gpt2-imdb") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use edbeeching/gpt2-imdb with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "edbeeching/gpt2-imdb" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "edbeeching/gpt2-imdb", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/edbeeching/gpt2-imdb
- SGLang
How to use edbeeching/gpt2-imdb 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 "edbeeching/gpt2-imdb" \ --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": "edbeeching/gpt2-imdb", "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 "edbeeching/gpt2-imdb" \ --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": "edbeeching/gpt2-imdb", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use edbeeching/gpt2-imdb with Docker Model Runner:
docker model run hf.co/edbeeching/gpt2-imdb
Librarian Bot: Add base_model information to model
This pull request aims to enrich the metadata of your model by adding gpt2 as a base_model field, situated in the YAML block of your model's README.md.
How did we find this information? We performed a regular expression match on your README.md file to determine the connection.
Why add this? Enhancing your model's metadata in this way:
- Boosts Discoverability - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub.
- Highlights Impact - It showcases the contributions and influences different models have within the community.
For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at librarian-bots/base_model_explorer.
This PR comes courtesy of Librarian Bot. If you have any feedback, queries, or need assistance, please don't hesitate to reach out to @davanstrien. Your input is invaluable to us!