Instructions to use effcot/Limo_llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use effcot/Limo_llama with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "effcot/Limo_llama") - Notebooks
- Google Colab
- Kaggle
File size: 349 Bytes
bef0a7f | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"epoch": 10.0,
"eval_loss": 0.7616215348243713,
"eval_runtime": 13.6304,
"eval_samples_per_second": 2.935,
"eval_steps_per_second": 0.734,
"total_flos": 3.853307493895635e+18,
"train_loss": 0.7713966806729634,
"train_runtime": 10792.2617,
"train_samples_per_second": 0.704,
"train_steps_per_second": 0.011
} |