Instructions to use nikitha2006/flan-t5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nikitha2006/flan-t5-base with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base") model = PeftModel.from_pretrained(base_model, "nikitha2006/flan-t5-base") - Notebooks
- Google Colab
- Kaggle
flan-t5-base
This model is a fine-tuned version of google/flan-t5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5939
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 12
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 7 | 3.4902 |
| 3.8973 | 2.0 | 14 | 2.1025 |
| 2.9243 | 3.0 | 21 | 1.7897 |
| 2.9243 | 4.0 | 28 | 1.3125 |
| 2.2081 | 5.0 | 35 | 0.9526 |
| 1.6758 | 6.0 | 42 | 0.8451 |
| 1.6758 | 7.0 | 49 | 0.6966 |
| 1.405 | 8.0 | 56 | 0.6964 |
| 1.1894 | 9.0 | 63 | 0.6179 |
| 1.0695 | 10.0 | 70 | 0.5889 |
| 1.0695 | 11.0 | 77 | 0.6051 |
| 1.0252 | 12.0 | 84 | 0.5939 |
Framework versions
- PEFT 0.14.0
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.0
- Tokenizers 0.21.0
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Base model
google/flan-t5-base