Instructions to use eabdullin/MathGenie-InterLM-20B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eabdullin/MathGenie-InterLM-20B-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="eabdullin/MathGenie-InterLM-20B-AWQ", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("eabdullin/MathGenie-InterLM-20B-AWQ", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e9ec0c8d707265c02346302f9cedf038322f99ef674c847b230df7e35731386
- Size of remote file:
- 2 GB
- SHA256:
- 0bbd469f5ac07073c9719d44a138ab35f2a72bf901975f64a070bd5dce3999cc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.