Instructions to use BigTMiami/amazon_pretraining_5M_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigTMiami/amazon_pretraining_5M_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BigTMiami/amazon_pretraining_5M_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BigTMiami/amazon_pretraining_5M_model") model = AutoModelForMaskedLM.from_pretrained("BigTMiami/amazon_pretraining_5M_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from BigTMiami/amazon_pretraining_5M_model: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/BigTMiami/amazon_pretraining_5M_model/resolve/main/training_args.bin
- Command line
-
hf download hf://BigTMiami/amazon_pretraining_5M_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BigTMiami/amazon_pretraining_5M_model/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- d83a6c09e475a2b7e11fd85326c62bd6fbdb6b80bd6acc38e1370c33ae5fe661
- Size of remote file:
- 4.92 kB
- SHA256:
- 1c118d5ac8397192b8ef95efc31efe77de4abc51b8604dfec01ca72c73026101
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.