Transformers
Safetensors
patchtst
time-series
remote-sensing
earth-observation
self-supervised-learning
masked-pretraining
open-weights
Instructions to use AminiTech/amini-fm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AminiTech/amini-fm with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, PatchTSTForPretraining tokenizer = AutoTokenizer.from_pretrained("AminiTech/amini-fm") model = PatchTSTForPretraining.from_pretrained("AminiTech/amini-fm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AminiTech/amini-fm: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/AminiTech/amini-fm/resolve/main/training_args.bin
- Command line
-
hf download hf://AminiTech/amini-fm/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AminiTech/amini-fm/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- f09d941866695f042effac0b5086cfa10bfde70f21b495666a04082f707238fb
- Size of remote file:
- 5.3 kB
- SHA256:
- 8c1a37751a55744e733d417cbe4ca88f6e1996054052f9380e4d8968af32d443
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