Instructions to use sid321axn/M2M101 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sid321axn/M2M101 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sid321axn/M2M101") model = AutoModelForSeq2SeqLM.from_pretrained("sid321axn/M2M101", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sid321axn/M2M101: direct link, hf CLI and curl.
- Browser
- Download file 1.94 GB
-
https://huggingface.co/sid321axn/M2M101/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sid321axn/M2M101/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sid321axn/M2M101/resolve/main/pytorch_model.bin
1.94 GB
- Xet hash:
- 1e0d5638fc412d3dba5309d6d80f5fc0b00c3433a55e1130460283cc954a77d8
- Size of remote file:
- 1.94 GB
- SHA256:
- eb3201adb85501b935d92250fb88212cbafcefe8084f27e82199cd8a391ed27a
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