Instructions to use masakhane/m2m100_418M_hau_en_rel_ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use masakhane/m2m100_418M_hau_en_rel_ft with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("masakhane/m2m100_418M_hau_en_rel_ft") model = AutoModelForSeq2SeqLM.from_pretrained("masakhane/m2m100_418M_hau_en_rel_ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from masakhane/m2m100_418M_hau_en_rel_ft: direct link, hf CLI and curl.
- Browser
- Download file 1.94 GB
-
https://huggingface.co/masakhane/m2m100_418M_hau_en_rel_ft/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://masakhane/m2m100_418M_hau_en_rel_ft/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/masakhane/m2m100_418M_hau_en_rel_ft/resolve/main/pytorch_model.bin
1.94 GB
- Xet hash:
- 8a50e8861c57aa95b8eec83c64fc085b45885eb171d239d6d91ea02025ba86c5
- Size of remote file:
- 1.94 GB
- SHA256:
- ef5c9bc9bb51a87105e85c6e7dedcdeaf31409fab0699631eb51872bd6667cf0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.