Instructions to use EthioNLP/EthioLLM-l-250K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EthioNLP/EthioLLM-l-250K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EthioNLP/EthioLLM-l-250K")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EthioNLP/EthioLLM-l-250K") model = AutoModelForMaskedLM.from_pretrained("EthioNLP/EthioLLM-l-250K", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from EthioNLP/EthioLLM-l-250K: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/EthioNLP/EthioLLM-l-250K/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://EthioNLP/EthioLLM-l-250K/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/EthioNLP/EthioLLM-l-250K/resolve/main/pytorch_model.bin
2.24 GB
- Xet hash:
- 26c0914fc1cbe5f37cce13f25b7808b272fc900b5c78803bc20f74f8d9c5e5d3
- Size of remote file:
- 2.24 GB
- SHA256:
- 783d25d3290c4196351ad0cb4101c0d63c0ae7af8908955d92978fe1b0ff1053
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