Instructions to use mllm-dev/gpt2_f_experiment_9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mllm-dev/gpt2_f_experiment_9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mllm-dev/gpt2_f_experiment_9")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt2_f_experiment_9") model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt2_f_experiment_9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c959f8b212043c4b851f095f11836f2fc9c031415682378243f3fd27cebf4fb
- Size of remote file:
- 996 MB
- SHA256:
- d5cd0e21d5c7463817612a4fe3ec5ca88eca9d379f08974ff2d7cb42ece47e0c
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