---
license: gemma
library_name: peft
tags:
- trl
- reward-trainer
- generated_from_trainer
base_model: google/gemma-2b
metrics:
- accuracy
model-index:
- name: RM-HH-AllMix_helpful_gpt3_loraR64_20000_gemma2b_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-AllMix_helpful_gpt3_loraR64_20000_gemma2b_shuffleTrue_extractchosenFalse

This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5220
- Accuracy: 0.7437

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7063        | 0.04  | 250   | 0.6784          | 0.5939   |
| 0.6441        | 0.08  | 500   | 0.6032          | 0.6613   |
| 0.582         | 0.13  | 750   | 0.5617          | 0.6921   |
| 0.5045        | 0.17  | 1000  | 0.5495          | 0.6985   |
| 0.5345        | 0.21  | 1250  | 0.5444          | 0.7034   |
| 0.53          | 0.25  | 1500  | 0.5522          | 0.7076   |
| 0.5325        | 0.29  | 1750  | 0.5550          | 0.7061   |
| 0.5145        | 0.33  | 2000  | 0.5596          | 0.7121   |
| 0.5156        | 0.38  | 2250  | 0.5480          | 0.7143   |
| 0.4995        | 0.42  | 2500  | 0.5477          | 0.7181   |
| 0.5329        | 0.46  | 2750  | 0.5350          | 0.7207   |
| 0.5037        | 0.5   | 3000  | 0.5472          | 0.7196   |
| 0.5417        | 0.54  | 3250  | 0.5233          | 0.7249   |
| 0.5179        | 0.59  | 3500  | 0.5230          | 0.7256   |
| 0.5264        | 0.63  | 3750  | 0.5196          | 0.7286   |
| 0.4931        | 0.67  | 4000  | 0.5267          | 0.7279   |
| 0.5114        | 0.71  | 4250  | 0.5202          | 0.7317   |
| 0.4735        | 0.75  | 4500  | 0.5238          | 0.7332   |
| 0.4902        | 0.79  | 4750  | 0.5294          | 0.7332   |
| 0.5483        | 0.84  | 5000  | 0.5165          | 0.7343   |
| 0.548         | 0.88  | 5250  | 0.5070          | 0.7350   |
| 0.4918        | 0.92  | 5500  | 0.5115          | 0.7384   |
| 0.5079        | 0.96  | 5750  | 0.5108          | 0.7369   |
| 0.49          | 1.0   | 6000  | 0.5127          | 0.7388   |
| 0.5161        | 1.05  | 6250  | 0.5103          | 0.7392   |
| 0.4573        | 1.09  | 6500  | 0.5226          | 0.7369   |
| 0.4973        | 1.13  | 6750  | 0.5208          | 0.7358   |
| 0.5163        | 1.17  | 7000  | 0.5135          | 0.7373   |
| 0.4857        | 1.21  | 7250  | 0.5188          | 0.7381   |
| 0.4996        | 1.25  | 7500  | 0.5200          | 0.7384   |
| 0.5029        | 1.3   | 7750  | 0.5185          | 0.7388   |
| 0.4983        | 1.34  | 8000  | 0.5177          | 0.7384   |
| 0.4718        | 1.38  | 8250  | 0.5186          | 0.7392   |
| 0.4723        | 1.42  | 8500  | 0.5204          | 0.7381   |
| 0.5238        | 1.46  | 8750  | 0.5143          | 0.7403   |
| 0.4613        | 1.51  | 9000  | 0.5178          | 0.7384   |
| 0.517         | 1.55  | 9250  | 0.5212          | 0.7377   |
| 0.495         | 1.59  | 9500  | 0.5181          | 0.7407   |
| 0.4865        | 1.63  | 9750  | 0.5191          | 0.7418   |
| 0.4799        | 1.67  | 10000 | 0.5231          | 0.7414   |
| 0.4546        | 1.71  | 10250 | 0.5241          | 0.7426   |
| 0.4673        | 1.76  | 10500 | 0.5256          | 0.7433   |
| 0.4598        | 1.8   | 10750 | 0.5259          | 0.7448   |
| 0.5035        | 1.84  | 11000 | 0.5245          | 0.7444   |
| 0.5113        | 1.88  | 11250 | 0.5236          | 0.7433   |
| 0.4821        | 1.92  | 11500 | 0.5230          | 0.7433   |
| 0.5071        | 1.97  | 11750 | 0.5220          | 0.7437   |


### Framework versions

- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2