Instructions to use Dikshant182004/t5-base-lora-finetune-tweetsumm-1760075742 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-1760075742 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-1760075742") - Transformers
How to use Dikshant182004/t5-base-lora-finetune-tweetsumm-1760075742 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Dikshant182004/t5-base-lora-finetune-tweetsumm-1760075742", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "all_params": 224673024, | |
| "epoch": 3.0, | |
| "memory_footprint": 898692096, | |
| "total_flos": 2506179136462848.0, | |
| "train_loss": 1.8389299175956033, | |
| "train_runtime": 453.2247, | |
| "train_samples_per_second": 5.818, | |
| "train_steps_per_second": 0.728, | |
| "trainable_params": 1769472, | |
| "trainable_params_percent": 0.7875765272113843 | |
| } |