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:
- 0e2438435d8908e0b5a22b3b433714cf4345b88a72d75091d4f9a641dd10eb22
- Size of remote file:
- 4.43 MB
- SHA256:
- ef11ac9a22c7503492f56d48dce53be20e339b63605983e9f27d2cd0e0f3922c
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