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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Mathoctopus/Parallel_33B") model = AutoModel.from_pretrained("Mathoctopus/Parallel_33B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model-00012-of-00014.bin from Mathoctopus/Parallel_33B: direct link, hf CLI and curl.
- Browser
- Download file 9.87 GB
-
https://huggingface.co/Mathoctopus/Parallel_33B/resolve/main/pytorch_model-00012-of-00014.bin
- Command line
-
hf download hf://Mathoctopus/Parallel_33B/pytorch_model-00012-of-00014.bin
-
curl -L -o pytorch_model-00012-of-00014.bin https://huggingface.co/Mathoctopus/Parallel_33B/resolve/main/pytorch_model-00012-of-00014.bin
9.87 GB
- Xet hash:
- 27ba6601db4855148e9a9ba4b173afc87a31924f72802c325b9a8b97c14f8af5
- Size of remote file:
- 9.87 GB
- SHA256:
- 3e9991d3b1af696e6d84d410c7e7b8d78d0fb1a24c0fbef1866e899490de8592
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.