Instructions to use samgreen/madlad400-10b-mt-ct2-int8_bfloat16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samgreen/madlad400-10b-mt-ct2-int8_bfloat16 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("samgreen/madlad400-10b-mt-ct2-int8_bfloat16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.bin from samgreen/madlad400-10b-mt-ct2-int8_bfloat16: direct link, hf CLI and curl.
- Browser
- Download file 10.7 GB
-
https://huggingface.co/samgreen/madlad400-10b-mt-ct2-int8_bfloat16/resolve/main/model.bin
- Command line
-
hf download hf://samgreen/madlad400-10b-mt-ct2-int8_bfloat16/model.bin
-
curl -L -o model.bin https://huggingface.co/samgreen/madlad400-10b-mt-ct2-int8_bfloat16/resolve/main/model.bin
10.7 GB
- Xet hash:
- c56b09f807dfa06189d5ca7a29dddf5765781a9e4454cf9a2cb88e056e43a367
- Size of remote file:
- 10.7 GB
- SHA256:
- 3168104dd81d5615df33df4ebb0d137c8f224c36d0fab56631cb6ce3020c235a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.