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
Commit ·
a78477f
1
Parent(s): bd00e60
End of training
Browse files- all_results.json +8 -0
- train_results.json +8 -0
- trainer_state.json +31 -0
all_results.json
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{
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"epoch": 1.0,
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"train_loss": 3.6124273203485022,
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"train_runtime": 523.8191,
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"train_samples": 6941,
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"train_steps_per_second": 1.657
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}
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train_results.json
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{
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"epoch": 1.0,
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"train_loss": 3.6124273203485022,
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"train_runtime": 523.8191,
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"train_samples": 6941,
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"train_samples_per_second": 13.251,
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"train_steps_per_second": 1.657
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}
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trainer_state.json
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{
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"best_metric": null,
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"best_model_checkpoint": null,
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"epoch": 1.0,
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"global_step": 868,
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"loss": 3.6339,
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"step": 500
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},
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{
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"epoch": 1.0,
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"step": 868,
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"total_flos": 3627255988224000.0,
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"train_loss": 3.6124273203485022,
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"train_runtime": 523.8191,
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"train_steps_per_second": 1.657
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}
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],
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"max_steps": 868,
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"num_train_epochs": 1,
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"trial_name": null,
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}
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