Instructions to use declare-lab/flan-alpaca-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use declare-lab/flan-alpaca-large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("declare-lab/flan-alpaca-large") model = AutoModelForSeq2SeqLM.from_pretrained("declare-lab/flan-alpaca-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from declare-lab/flan-alpaca-large: direct link, hf CLI and curl.
- Browser
- Download file 3.13 GB
-
https://huggingface.co/declare-lab/flan-alpaca-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://declare-lab/flan-alpaca-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/declare-lab/flan-alpaca-large/resolve/main/pytorch_model.bin
3.13 GB
- Xet hash:
- a71e2eea81dfe32157b619e47e7972d4e82c062e90853cf79fe93c45b5772320
- Size of remote file:
- 3.13 GB
- SHA256:
- 6ac6584aa49a6ecc6f2407030a3d798278bde5540177c9533beb4726e95e6048
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.