Instructions to use meghumaggie/flan-t5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use meghumaggie/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, "meghumaggie/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: 4.0202
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 7 | 26.7172 |
| 32.6479 | 2.0 | 14 | 18.1347 |
| 18.8366 | 3.0 | 21 | 9.7445 |
| 18.8366 | 4.0 | 28 | 5.0479 |
| 7.9998 | 5.0 | 35 | 4.5527 |
| 4.9917 | 6.0 | 42 | 4.3575 |
| 4.9917 | 7.0 | 49 | 4.2304 |
| 4.5887 | 8.0 | 56 | 4.1260 |
| 4.4006 | 9.0 | 63 | 4.0510 |
| 4.2913 | 10.0 | 70 | 4.0202 |
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