Instructions to use jiosephlee/rejection_sampled_intern with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jiosephlee/rejection_sampled_intern with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jiosephlee/rejection_sampled_intern", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jiosephlee/rejection_sampled_intern", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
jiosephlee/sft_rejection_sampling_pgb_clin_herg_Intern-s1-mini-distill-dsv32-11k-samples_lr1e-05
a958d1b verified Download tokenizer_IUPAC.model from jiosephlee/rejection_sampled_intern: direct link, hf CLI and curl.
- Browser
- Download file 5.9 kB
-
https://huggingface.co/jiosephlee/rejection_sampled_intern/resolve/main/tokenizer_IUPAC.model
- Command line
-
hf download hf://jiosephlee/rejection_sampled_intern/tokenizer_IUPAC.model
-
curl -L -o tokenizer_IUPAC.model https://huggingface.co/jiosephlee/rejection_sampled_intern/resolve/main/tokenizer_IUPAC.model
5.9 kB
- Xet hash:
- a6b5c578315e2d6aef8af99243d23549e06a08d6c5f319199b17a373433993d4
- Size of remote file:
- 5.9 kB
- SHA256:
- 6e719023a50767e2da1165925feb3afe77d63702f08d0cd39c4ddadba7cdaaca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.