---
license: gemma
library_name: peft
tags:
- trl
- reward-trainer
- generated_from_trainer
metrics:
- accuracy
base_model: google/gemma-2b
model-index:
- name: RM-HH-Gemma_harmless_human_20000_gemma2b
  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_harmless_human_20000_gemma2b

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: 0.6196
- Accuracy: 0.6678

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7635        | 0.06  | 250  | 0.7536          | 0.4972   |
| 0.7325        | 0.11  | 500  | 0.7235          | 0.5378   |
| 0.7285        | 0.17  | 750  | 0.7007          | 0.5673   |
| 0.7183        | 0.22  | 1000 | 0.6823          | 0.5973   |
| 0.6913        | 0.28  | 1250 | 0.6703          | 0.6113   |
| 0.6918        | 0.33  | 1500 | 0.6594          | 0.6263   |
| 0.6673        | 0.39  | 1750 | 0.6509          | 0.6368   |
| 0.6753        | 0.44  | 2000 | 0.6432          | 0.6398   |
| 0.6616        | 0.5   | 2250 | 0.6382          | 0.6503   |
| 0.672         | 0.56  | 2500 | 0.6336          | 0.6488   |
| 0.6457        | 0.61  | 2750 | 0.6308          | 0.6533   |
| 0.6705        | 0.67  | 3000 | 0.6276          | 0.6563   |
| 0.6534        | 0.72  | 3250 | 0.6247          | 0.6593   |
| 0.6176        | 0.78  | 3500 | 0.6228          | 0.6623   |
| 0.6597        | 0.83  | 3750 | 0.6210          | 0.6643   |
| 0.6404        | 0.89  | 4000 | 0.6201          | 0.6663   |
| 0.6199        | 0.94  | 4250 | 0.6196          | 0.6678   |


### Framework versions

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