Instructions to use mllm-dev/gpt2_f_experiment_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mllm-dev/gpt2_f_experiment_0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mllm-dev/gpt2_f_experiment_0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt2_f_experiment_0") model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt2_f_experiment_0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f2ced87b2eb8e1a07aa8dd1de83ed55ca119fcd8e9c1ac36a01e78b8763a5cc
- Size of remote file:
- 996 MB
- SHA256:
- 6f323d435297d2e588b52b8ab08bc2d9e4cd4ebd8c1b20e81931572e9289285c
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