Instructions to use mllm-dev/gpt2_f_experiment_8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mllm-dev/gpt2_f_experiment_8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mllm-dev/gpt2_f_experiment_8")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt2_f_experiment_8") model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt2_f_experiment_8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9546e4760cc0b1c20cbe83c7553d120fb4eff5dbffc08b02d5a1f48a5e767ca8
- Size of remote file:
- 996 MB
- SHA256:
- 2e7d45a7eb590b7453a36a09089d7c256ee7853b9db41f38f5f10286d210ba2b
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