Instructions to use Dikshant182004/t5-base-lora-finetune-tweetsumm-1759927258 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Dikshant182004/t5-base-lora-finetune-tweetsumm-1759927258 with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base") model = PeftModel.from_pretrained(base_model, "Dikshant182004/t5-base-lora-finetune-tweetsumm-1759927258") - Transformers
How to use Dikshant182004/t5-base-lora-finetune-tweetsumm-1759927258 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Dikshant182004/t5-base-lora-finetune-tweetsumm-1759927258", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 351 Bytes
8560bbc | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"all_params": 224673024,
"epoch": 3.0,
"memory_footprint": 898692096,
"total_flos": 2506179136462848.0,
"train_loss": 1.8666806372729214,
"train_runtime": 383.5951,
"train_samples_per_second": 6.874,
"train_steps_per_second": 0.86,
"trainable_params": 1769472,
"trainable_params_percent": 0.7875765272113843
} |