Instructions to use DuoGuard/DuoGuard-1B-Llama-3.2-transfer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DuoGuard/DuoGuard-1B-Llama-3.2-transfer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DuoGuard/DuoGuard-1B-Llama-3.2-transfer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DuoGuard/DuoGuard-1B-Llama-3.2-transfer") model = AutoModelForSequenceClassification.from_pretrained("DuoGuard/DuoGuard-1B-Llama-3.2-transfer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from DuoGuard/DuoGuard-1B-Llama-3.2-transfer: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/DuoGuard/DuoGuard-1B-Llama-3.2-transfer/resolve/main/training_args.bin
- Command line
-
hf download hf://DuoGuard/DuoGuard-1B-Llama-3.2-transfer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/DuoGuard/DuoGuard-1B-Llama-3.2-transfer/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- 6ddd3d5392cb645dfe9ef98adf50aac495bdf764c55ba7113b9f4f2a34d493ff
- Size of remote file:
- 5.18 kB
- SHA256:
- 4c7a13dc5520e04eaeecc73dcee5b2848fc0add1ba6942aef40c8e7f52458858
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.