Instructions to use mllm-dev/gpt_f_experiment_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mllm-dev/gpt_f_experiment_large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mllm-dev/gpt_f_experiment_large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt_f_experiment_large") model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt_f_experiment_large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4529261797f14a284b3d559cdabd8e8df9727dbbcfc149adb09c55e8a4a7d2aa
- Size of remote file:
- 12.5 GB
- SHA256:
- d6fa81ba98739d7e4d636f96a25e273984133de1c10c50fa2595ca20d70aa18b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.