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:
- 63b075f815cf7ca8b56315effe335421eb1667b47ad0f9b1f15fa7131d564104
- Size of remote file:
- 4.54 kB
- SHA256:
- cf5cfa22d0f90ada96e513d3b5dcc0a6518f2f743472fa1ed8eb4d0a50dbee5b
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