Instructions to use EthioNLP/EthioLLM-b-250K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EthioNLP/EthioLLM-b-250K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EthioNLP/EthioLLM-b-250K")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EthioNLP/EthioLLM-b-250K") model = AutoModelForMaskedLM.from_pretrained("EthioNLP/EthioLLM-b-250K", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sentencepiece.bpe.model from EthioNLP/EthioLLM-b-250K: direct link, hf CLI and curl.
- Browser
- Download file 5.61 MB
-
https://huggingface.co/EthioNLP/EthioLLM-b-250K/resolve/main/sentencepiece.bpe.model
- Command line
-
hf download hf://EthioNLP/EthioLLM-b-250K/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/EthioNLP/EthioLLM-b-250K/resolve/main/sentencepiece.bpe.model
5.61 MB
- Xet hash:
- bb245e0098dad77af984ee4e1e9530ed072c3be760436270dca898ccf44a94f7
- Size of remote file:
- 5.61 MB
- SHA256:
- b775eefca433d144d14818647186b2d7d0467f444afe0e11b4e3141bd83f2ef3
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