Instructions to use Joshikaa/flan-t5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Joshikaa/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, "Joshikaa/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: 8.8301
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: 15
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 7 | 9.1803 |
| 9.3683 | 2.0 | 14 | 9.1743 |
| 9.2102 | 3.0 | 21 | 9.0925 |
| 9.2102 | 4.0 | 28 | 8.9160 |
| 9.1206 | 5.0 | 35 | 8.8353 |
| 9.1677 | 6.0 | 42 | 8.8664 |
| 9.1677 | 7.0 | 49 | 8.8229 |
| 9.0556 | 8.0 | 56 | 8.9306 |
| 8.891 | 9.0 | 63 | 8.8649 |
| 9.0597 | 10.0 | 70 | 8.8148 |
| 9.0597 | 11.0 | 77 | 8.8811 |
| 9.0373 | 12.0 | 84 | 8.9017 |
| 8.8946 | 13.0 | 91 | 8.8119 |
| 8.8946 | 14.0 | 98 | 8.8853 |
| 9.0261 | 15.0 | 105 | 8.8301 |
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