Instructions to use erbacher/flanlarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use erbacher/flanlarge with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("erbacher/flanlarge") model = AutoModelForSeq2SeqLM.from_pretrained("erbacher/flanlarge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from erbacher/flanlarge: direct link, hf CLI and curl.
- Browser
- Download file 3.13 GB
-
https://huggingface.co/erbacher/flanlarge/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://erbacher/flanlarge/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/erbacher/flanlarge/resolve/main/pytorch_model.bin
3.13 GB
- Xet hash:
- 8010f9b5ced05aeff004c593394dbf364a7f87e5f5d98410b641c030c0df4a20
- Size of remote file:
- 3.13 GB
- SHA256:
- e9f390791dbf9fa1edf7ad5a563938293ca8504932de57c480414b1a39d07033
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.