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---
license: mit
library_name: peft
tags:
- trl
- reward-trainer
- generated_from_trainer
base_model: openai-community/gpt2-large
metrics:
- accuracy
model-index:
- name: RM-HH-Gemma_helpful_human_loraR64_20000_gpt2-large_shuffleTrue_extractchosenFalse
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. -->
# RM-HH-Gemma_helpful_human_loraR64_20000_gpt2-large_shuffleTrue_extractchosenFalse
This model is a fine-tuned version of [openai-community/gpt2-large](https://huggingface.co./openai-community/gpt2-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6198
- Accuracy: 0.6546
## 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: 1.41e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.703 | 0.06 | 250 | 0.7010 | 0.5414 |
| 0.6903 | 0.11 | 500 | 0.6820 | 0.5584 |
| 0.6715 | 0.17 | 750 | 0.6700 | 0.5805 |
| 0.6498 | 0.22 | 1000 | 0.6604 | 0.5985 |
| 0.6625 | 0.28 | 1250 | 0.6550 | 0.6070 |
| 0.6425 | 0.33 | 1500 | 0.6523 | 0.6170 |
| 0.6514 | 0.39 | 1750 | 0.6480 | 0.6226 |
| 0.6535 | 0.45 | 2000 | 0.6454 | 0.6256 |
| 0.6233 | 0.5 | 2250 | 0.6427 | 0.6296 |
| 0.6434 | 0.56 | 2500 | 0.6403 | 0.6306 |
| 0.6288 | 0.61 | 2750 | 0.6379 | 0.6421 |
| 0.632 | 0.67 | 3000 | 0.6360 | 0.6361 |
| 0.6365 | 0.72 | 3250 | 0.6337 | 0.6436 |
| 0.6329 | 0.78 | 3500 | 0.6322 | 0.6456 |
| 0.6278 | 0.84 | 3750 | 0.6312 | 0.6441 |
| 0.6369 | 0.89 | 4000 | 0.6299 | 0.6476 |
| 0.6313 | 0.95 | 4250 | 0.6292 | 0.6466 |
| 0.6315 | 1.0 | 4500 | 0.6285 | 0.6456 |
| 0.6039 | 1.06 | 4750 | 0.6278 | 0.6441 |
| 0.6259 | 1.11 | 5000 | 0.6268 | 0.6471 |
| 0.629 | 1.17 | 5250 | 0.6259 | 0.6481 |
| 0.6201 | 1.23 | 5500 | 0.6251 | 0.6506 |
| 0.6118 | 1.28 | 5750 | 0.6251 | 0.6521 |
| 0.6125 | 1.34 | 6000 | 0.6243 | 0.6481 |
| 0.6115 | 1.39 | 6250 | 0.6236 | 0.6476 |
| 0.6056 | 1.45 | 6500 | 0.6234 | 0.6506 |
| 0.6255 | 1.5 | 6750 | 0.6224 | 0.6501 |
| 0.6314 | 1.56 | 7000 | 0.6215 | 0.6511 |
| 0.6346 | 1.62 | 7250 | 0.6211 | 0.6501 |
| 0.6269 | 1.67 | 7500 | 0.6207 | 0.6516 |
| 0.6104 | 1.73 | 7750 | 0.6204 | 0.6526 |
| 0.6138 | 1.78 | 8000 | 0.6202 | 0.6526 |
| 0.6172 | 1.84 | 8250 | 0.6201 | 0.6541 |
| 0.6149 | 1.89 | 8500 | 0.6199 | 0.6541 |
| 0.6022 | 1.95 | 8750 | 0.6198 | 0.6546 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 |