Instructions to use edbeeching/gpt2-medium-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edbeeching/gpt2-medium-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="edbeeching/gpt2-medium-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("edbeeching/gpt2-medium-imdb") model = AutoModelForCausalLM.from_pretrained("edbeeching/gpt2-medium-imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use edbeeching/gpt2-medium-imdb with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "edbeeching/gpt2-medium-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-medium-imdb", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/edbeeching/gpt2-medium-imdb
- SGLang
How to use edbeeching/gpt2-medium-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-medium-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-medium-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-medium-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-medium-imdb", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use edbeeching/gpt2-medium-imdb with Docker Model Runner:
docker model run hf.co/edbeeching/gpt2-medium-imdb
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 1.0, | |
| "global_step": 913, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.55, | |
| "learning_rate": 2.2617743702081052e-05, | |
| "loss": 3.3284, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "step": 913, | |
| "total_flos": 1.3566459745665024e+16, | |
| "train_loss": 3.314140929841525, | |
| "train_runtime": 946.0478, | |
| "train_samples_per_second": 7.721, | |
| "train_steps_per_second": 0.965 | |
| } | |
| ], | |
| "max_steps": 913, | |
| "num_train_epochs": 1, | |
| "total_flos": 1.3566459745665024e+16, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |