Text Classification
Transformers
Safetensors
llama
trl
reward-trainer
reward-model
creative-writing
text-embeddings-inference
Instructions to use SAA-Lab/Llama8B-CreativeWritingVerifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SAA-Lab/Llama8B-CreativeWritingVerifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SAA-Lab/Llama8B-CreativeWritingVerifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SAA-Lab/Llama8B-CreativeWritingVerifier") model = AutoModelForSequenceClassification.from_pretrained("SAA-Lab/Llama8B-CreativeWritingVerifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8e9d3e14829f0fead5573a33bb9be64d082a2c79768267da7e434217ed76d7c
- Size of remote file:
- 5.5 kB
- SHA256:
- b68ca4ed3c3ab2cf3e0fd7a08f2ee891a5ab5ec9f4b352dced3bc8e91d33c91b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.