Instructions to use mllm-dev/gpt2_f_experiment_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mllm-dev/gpt2_f_experiment_6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mllm-dev/gpt2_f_experiment_6")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt2_f_experiment_6") model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt2_f_experiment_6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f9b24cc7ce7293a724178c20fdf4ba3a3b94f44132ee01f32a54c5932bef0ab
- Size of remote file:
- 996 MB
- SHA256:
- 5fb5cc86e42f63c702e31d4e1ad6469017a3323b68f8c16f049e9b2ecd1467f6
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