Instructions to use TrustSafeAI/RADAR-Vicuna-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TrustSafeAI/RADAR-Vicuna-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TrustSafeAI/RADAR-Vicuna-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TrustSafeAI/RADAR-Vicuna-7B") model = AutoModelForSequenceClassification.from_pretrained("TrustSafeAI/RADAR-Vicuna-7B") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a5c7d73bb7d5e4da887fea278edfc02be5038401ecb93d9ba0153e1510ed4589
- Size of remote file:
- 1.42 GB
- SHA256:
- 4ea32c4a31b7004364df4fe672c5c763f3d5f32b7514aaeb2b5e47653bc89792
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.