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 training_args.bin from masakhane/m2m100_418M_hau_en_rel_ft: direct link, hf CLI and curl.
- Browser
- Download file 2.74 kB
-
https://huggingface.co/masakhane/m2m100_418M_hau_en_rel_ft/resolve/main/training_args.bin
- Command line
-
hf download hf://masakhane/m2m100_418M_hau_en_rel_ft/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/masakhane/m2m100_418M_hau_en_rel_ft/resolve/main/training_args.bin
2.74 kB
- Xet hash:
- 8f4addc266ec4c9869f11c0ae77fa6a9f28bfcc759978dd7fdf6e77d5e094e0a
- Size of remote file:
- 2.74 kB
- SHA256:
- 810c3c6aba9b9f7b4891f7888732718742a553831f2ef68754495860f563c150
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