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:
- 558e5dc1ac74e4906fafab9c56e0666918caa584666fb98647f2bab92bb39196
- Size of remote file:
- 9.75 GB
- SHA256:
- fa2b48a9262ee6ff3a24902cf45ded5db40c97611d3cf500c06507e9a44c4bba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.