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 eval_results.json from EthioNLP/EthioLLM-l-250K: direct link, hf CLI and curl.
- Browser
- Download file 231 Bytes
-
https://huggingface.co/EthioNLP/EthioLLM-l-250K/resolve/21cf7ee2d6f7abdbfddbce97cd3b7a5716d25848/eval_results.json
- Command line
-
hf download hf://EthioNLP/EthioLLM-l-250K@21cf7ee2d6f7abdbfddbce97cd3b7a5716d25848/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/EthioNLP/EthioLLM-l-250K/resolve/21cf7ee2d6f7abdbfddbce97cd3b7a5716d25848/eval_results.json
231 Bytes
| { | |
| "epoch": 10.0, | |
| "eval_loss": 3.0551953315734863, | |
| "eval_runtime": 1549.8799, | |
| "eval_samples": 146099, | |
| "eval_samples_per_second": 94.265, | |
| "eval_steps_per_second": 11.783, | |
| "perplexity": 21.225331099339662 | |
| } |