Instructions to use hundredeuk2/rm_opt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hundredeuk2/rm_opt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hundredeuk2/rm_opt")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hundredeuk2/rm_opt") model = AutoModelForSequenceClassification.from_pretrained("hundredeuk2/rm_opt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hundredeuk2/rm_opt: direct link, hf CLI and curl.
- Browser
- Download file 5.26 GB
-
https://huggingface.co/hundredeuk2/rm_opt/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hundredeuk2/rm_opt/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hundredeuk2/rm_opt/resolve/main/pytorch_model.bin
5.26 GB
- Xet hash:
- 0f13ce3f619829e3969f1b2b15b5fd61b4a5a49c90470abaf27a471b381ff4f4
- Size of remote file:
- 5.26 GB
- SHA256:
- 9f739f0f40b1ad8138469d9a5e4d5f414a32b1736196f3c70233123aa79c4779
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