Instructions to use mllm-dev/gpt2_f_experiment_7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mllm-dev/gpt2_f_experiment_7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mllm-dev/gpt2_f_experiment_7")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt2_f_experiment_7") model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt2_f_experiment_7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c84947e8701f5373c8ede3b476dae29559b8a29db710da69f5aa096d1f630612
- Size of remote file:
- 996 MB
- SHA256:
- b42794288551215d43a2c9a2bde3898f01fc928597aa0c9a50d4430fe5a90d9a
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