Instructions to use RamAnanth1/positive-reframing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RamAnanth1/positive-reframing with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("RamAnanth1/positive-reframing") model = AutoModelForSeq2SeqLM.from_pretrained("RamAnanth1/positive-reframing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from RamAnanth1/positive-reframing: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/RamAnanth1/positive-reframing/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RamAnanth1/positive-reframing/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RamAnanth1/positive-reframing/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- 5eba6119a434a626bf8c15d4ed8bbb51972742cd92acf2a911612ad6b3c62ab8
- Size of remote file:
- 1.63 GB
- SHA256:
- 441f3ebb7b145b377eeae9eaf7b317a3a83e7f13c77f3f41c2276df2e41daa90
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.