Instructions to use Mathoctopus/Parallel_33B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mathoctopus/Parallel_33B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Mathoctopus/Parallel_33B")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Mathoctopus/Parallel_33B") model = AutoModel.from_pretrained("Mathoctopus/Parallel_33B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0e5f1994a76718f1d9bd5731ccbc7fb81a6afa701e7783ee3bc127924325917
- Size of remote file:
- 9.87 GB
- SHA256:
- 1bd62d752b302acf655c28ac16ef2613668499d38bbdabae275129cb1ca8e5f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.