---
license: gemma
library_name: peft
tags:
- trl
- reward-trainer
- generated_from_trainer
base_model: google/gemma-2b
metrics:
- accuracy
model-index:
- name: RM-HH-Gemma_helpful_human_20000_gemma2b_shuffleFalse_extractchosenTrue
  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_20000_gemma2b_shuffleFalse_extractchosenTrue

This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6203
- Accuracy: 0.6600

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.733         | 0.06  | 250  | 0.7295          | 0.5123   |
| 0.7034        | 0.11  | 500  | 0.7061          | 0.5498   |
| 0.7165        | 0.17  | 750  | 0.6913          | 0.5729   |
| 0.6821        | 0.22  | 1000 | 0.6809          | 0.5884   |
| 0.6707        | 0.28  | 1250 | 0.6744          | 0.6024   |
| 0.6551        | 0.33  | 1500 | 0.6692          | 0.6114   |
| 0.6744        | 0.39  | 1750 | 0.6617          | 0.6144   |
| 0.6418        | 0.45  | 2000 | 0.6591          | 0.6335   |
| 0.6546        | 0.5   | 2250 | 0.6567          | 0.6355   |
| 0.6465        | 0.56  | 2500 | 0.6537          | 0.6375   |
| 0.6489        | 0.61  | 2750 | 0.6471          | 0.6390   |
| 0.6555        | 0.67  | 3000 | 0.6399          | 0.6390   |
| 0.647         | 0.72  | 3250 | 0.6374          | 0.6460   |
| 0.6555        | 0.78  | 3500 | 0.6365          | 0.6490   |
| 0.6165        | 0.83  | 3750 | 0.6347          | 0.6465   |
| 0.6385        | 0.89  | 4000 | 0.6338          | 0.6485   |
| 0.6202        | 0.95  | 4250 | 0.6317          | 0.6490   |
| 0.6198        | 1.0   | 4500 | 0.6316          | 0.6520   |
| 0.6092        | 1.06  | 4750 | 0.6325          | 0.6515   |
| 0.6091        | 1.11  | 5000 | 0.6339          | 0.6510   |
| 0.605         | 1.17  | 5250 | 0.6338          | 0.6540   |
| 0.673         | 1.22  | 5500 | 0.6263          | 0.6550   |
| 0.6119        | 1.28  | 5750 | 0.6267          | 0.6565   |
| 0.6153        | 1.34  | 6000 | 0.6267          | 0.6580   |
| 0.6048        | 1.39  | 6250 | 0.6249          | 0.6560   |
| 0.62          | 1.45  | 6500 | 0.6228          | 0.6540   |
| 0.6213        | 1.5   | 6750 | 0.6234          | 0.6595   |
| 0.6107        | 1.56  | 7000 | 0.6228          | 0.6605   |
| 0.6266        | 1.61  | 7250 | 0.6212          | 0.6580   |
| 0.6088        | 1.67  | 7500 | 0.6211          | 0.6595   |
| 0.6282        | 1.72  | 7750 | 0.6210          | 0.6615   |
| 0.6384        | 1.78  | 8000 | 0.6197          | 0.6610   |
| 0.5987        | 1.84  | 8250 | 0.6198          | 0.6580   |
| 0.5911        | 1.89  | 8500 | 0.6201          | 0.6600   |
| 0.5981        | 1.95  | 8750 | 0.6203          | 0.6600   |


### Framework versions

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