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---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: google/flan-t5-large
model-index:
- name: LoRA-FlanT5-large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# LoRA-FlanT5-large
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0791
## 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: 5e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9842 | 0.24 | 250 | 0.0915 |
| 0.1063 | 0.48 | 500 | 0.0848 |
| 0.1006 | 0.72 | 750 | 0.0823 |
| 0.0991 | 0.96 | 1000 | 0.0812 |
| 0.0979 | 1.2 | 1250 | 0.0805 |
| 0.0966 | 1.44 | 1500 | 0.0801 |
| 0.0946 | 1.69 | 1750 | 0.0798 |
| 0.0965 | 1.93 | 2000 | 0.0797 |
| 0.0964 | 2.17 | 2250 | 0.0795 |
| 0.0953 | 2.41 | 2500 | 0.0794 |
| 0.095 | 2.65 | 2750 | 0.0793 |
| 0.0958 | 2.89 | 3000 | 0.0792 |
| 0.0952 | 3.13 | 3250 | 0.0792 |
| 0.095 | 3.37 | 3500 | 0.0792 |
| 0.0966 | 3.61 | 3750 | 0.0792 |
| 0.0948 | 3.85 | 4000 | 0.0792 |
| 0.0954 | 4.09 | 4250 | 0.0791 |
| 0.0944 | 4.33 | 4500 | 0.0792 |
| 0.0947 | 4.57 | 4750 | 0.0791 |
| 0.0962 | 4.81 | 5000 | 0.0791 |
| 0.0947 | 5.06 | 5250 | 0.0792 |
| 0.0943 | 5.3 | 5500 | 0.0791 |
| 0.0946 | 5.54 | 5750 | 0.0792 |
| 0.0956 | 5.78 | 6000 | 0.0791 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.15.2 |