Instructions to use Heng666/madlad400-10b-mt-ct2-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Heng666/madlad400-10b-mt-ct2-int8 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Heng666/madlad400-10b-mt-ct2-int8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fee7ce55021570e2652ffa302843c74644b43ddec1d37bb220ca1f7efc90149c
- Size of remote file:
- 10.7 GB
- SHA256:
- 2d5df751c281f4480cd45529b88f7b7dfff7e387469d619d37b986a00150734d
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