Instructions to use eabdullin/internlm2-math-20b-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eabdullin/internlm2-math-20b-awq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="eabdullin/internlm2-math-20b-awq", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("eabdullin/internlm2-math-20b-awq", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download inputs_stats.pth from eabdullin/internlm2-math-20b-awq: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/eabdullin/internlm2-math-20b-awq/resolve/main/inputs_stats.pth
- Command line
-
hf download hf://eabdullin/internlm2-math-20b-awq/inputs_stats.pth
-
curl -L -o inputs_stats.pth https://huggingface.co/eabdullin/internlm2-math-20b-awq/resolve/main/inputs_stats.pth
20 MB
- Xet hash:
- af7a2bbc2aa01aa197060dacf83efb9a9b496ea8c49353b8dc9c7ffe3379e2f0
- Size of remote file:
- 20 MB
- SHA256:
- 8280be31c34a0650fcb1e8cb0e7c9915251c2b4b2f7788ca9e50f57a73b1af9b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.