---
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_harmless_gpt3_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-AllMix_harmless_gpt3_20000_gemma2b_shuffleFalse_extractchosenTrue

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: 0.1748
- Accuracy: 0.9237

## 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: 1.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7416        | 0.04  | 250  | 0.5877          | 0.7074   |
| 0.7004        | 0.08  | 500  | 0.4614          | 0.7980   |
| 0.6298        | 0.13  | 750  | 0.3453          | 0.8477   |
| 0.5979        | 0.17  | 1000 | 0.2723          | 0.8774   |
| 0.5842        | 0.21  | 1250 | 0.2469          | 0.8868   |
| 0.6257        | 0.25  | 1500 | 0.2255          | 0.8973   |
| 0.5833        | 0.29  | 1750 | 0.2103          | 0.9071   |
| 0.6368        | 0.33  | 2000 | 0.2061          | 0.9082   |
| 0.5854        | 0.38  | 2250 | 0.2063          | 0.9105   |
| 0.5458        | 0.42  | 2500 | 0.1990          | 0.9127   |
| 0.6079        | 0.46  | 2750 | 0.1993          | 0.9135   |
| 0.5819        | 0.5   | 3000 | 0.1917          | 0.9165   |
| 0.5823        | 0.54  | 3250 | 0.1844          | 0.9180   |
| 0.618         | 0.59  | 3500 | 0.1869          | 0.9188   |
| 0.6075        | 0.63  | 3750 | 0.1885          | 0.9169   |
| 0.5685        | 0.67  | 4000 | 0.1848          | 0.9191   |
| 0.5718        | 0.71  | 4250 | 0.1848          | 0.9206   |
| 0.5697        | 0.75  | 4500 | 0.1819          | 0.9210   |
| 0.5719        | 0.79  | 4750 | 0.1769          | 0.9229   |
| 0.5774        | 0.84  | 5000 | 0.1779          | 0.9218   |
| 0.5331        | 0.88  | 5250 | 0.1745          | 0.9233   |
| 0.564         | 0.92  | 5500 | 0.1752          | 0.9237   |
| 0.567         | 0.96  | 5750 | 0.1748          | 0.9237   |


### Framework versions

- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2