Instructions to use illian64/madlad400-7b-mt-ct2-bfloat16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use illian64/madlad400-7b-mt-ct2-bfloat16 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="illian64/madlad400-7b-mt-ct2-bfloat16")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("illian64/madlad400-7b-mt-ct2-bfloat16", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30a2e2c253e2803adea0cb49ff4cdad06c9ebcbc5f1e5596d8d1bbd05e513c0e
- Size of remote file:
- 16.6 GB
- SHA256:
- 4567d605ef4dc39128d34498bca735b44a69686f838b3870e9d6803648455285
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.