Instructions to use mllm-dev/gpt2_f_experiment_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mllm-dev/gpt2_f_experiment_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mllm-dev/gpt2_f_experiment_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt2_f_experiment_2") model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt2_f_experiment_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f3356f22c4b8c98170d41c370e4974171fa511ad9f1359a34c664e48df5952d
- Size of remote file:
- 4.54 kB
- SHA256:
- 033054f6387e13806b217d1835e983ebdff5fe6adf4986538b326767eb56b790
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