Instructions to use parameterlab/apricot_clustering_coqa_deberta-v3-base_for_vicuna-7b-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use parameterlab/apricot_clustering_coqa_deberta-v3-base_for_vicuna-7b-v1.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="parameterlab/apricot_clustering_coqa_deberta-v3-base_for_vicuna-7b-v1.5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("parameterlab/apricot_clustering_coqa_deberta-v3-base_for_vicuna-7b-v1.5") model = AutoModelForSequenceClassification.from_pretrained("parameterlab/apricot_clustering_coqa_deberta-v3-base_for_vicuna-7b-v1.5") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert
This new file is equivalent to pytorch_model.bin but safe in the sense that
no arbitrary code can be put into it.
These files also happen to load much faster than their pytorch counterpart:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb
The widgets on your model page will run using this model even if this is not merged
making sure the file actually works.
If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions
Feel free to ignore this PR.