Instructions to use LifeAi-dev/madlad400-10b-ct2-float32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LifeAi-dev/madlad400-10b-ct2-float32 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="LifeAi-dev/madlad400-10b-ct2-float32")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LifeAi-dev/madlad400-10b-ct2-float32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from LifeAi-dev/madlad400-10b-ct2-float32: direct link, hf CLI and curl.
- Browser
- Download file 16.6 MB
-
https://huggingface.co/LifeAi-dev/madlad400-10b-ct2-float32/resolve/main/tokenizer.json
- Command line
-
hf download hf://LifeAi-dev/madlad400-10b-ct2-float32/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/LifeAi-dev/madlad400-10b-ct2-float32/resolve/main/tokenizer.json
16.6 MB
- Xet hash:
- 828d0ef75e270ab2aeb2358cba3a69db8b24ab9c479fe2847c89b36163f76aa9
- Size of remote file:
- 16.6 MB
- SHA256:
- a2799ccc696b752ba00c34f58726bfe253a04921ceb6cfc620400f560474790b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.