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:
- e1f0f3b8411d9afc4fecb1c7e9023f26b3f8833529174c7bd4baa85e667ff524
- Size of remote file:
- 996 MB
- SHA256:
- e3b5468617dd897406730aa6e6d65a0cb7ad4a15dbd653f2d4f9c9e7fc9cc72d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.