Instructions to use SCS/Fine-tuned-PCSD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SCS/Fine-tuned-PCSD with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SCS/Fine-tuned-PCSD") model = AutoModelForSeq2SeqLM.from_pretrained("SCS/Fine-tuned-PCSD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SCS/Fine-tuned-PCSD: direct link, hf CLI and curl.
- Browser
- Download file 1.62 GB
-
https://huggingface.co/SCS/Fine-tuned-PCSD/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SCS/Fine-tuned-PCSD/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SCS/Fine-tuned-PCSD/resolve/main/pytorch_model.bin
1.62 GB
- Xet hash:
- 6b22b9825cadada825513fa9575d1e66c912d86aa3524691b7f507d492adbe6e
- Size of remote file:
- 1.62 GB
- SHA256:
- a6c37dc10fa43a83c12d49e1d0de93e6ac16217440ad62e119ab75ca8d5e009e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.