---
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_shuffleTrue_extractchosenFalse
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# RM-HH-AllMix_harmless_gpt3_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: 0.5035
- Accuracy: 0.7281

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6954        | 0.04  | 250  | 0.6616          | 0.5961   |
| 0.6049        | 0.08  | 500  | 0.5946          | 0.6491   |
| 0.559         | 0.13  | 750  | 0.5750          | 0.6657   |
| 0.5508        | 0.17  | 1000 | 0.5561          | 0.6800   |
| 0.5562        | 0.21  | 1250 | 0.5426          | 0.6886   |
| 0.5167        | 0.25  | 1500 | 0.5325          | 0.6916   |
| 0.5202        | 0.29  | 1750 | 0.5269          | 0.7029   |
| 0.4852        | 0.33  | 2000 | 0.5255          | 0.7070   |
| 0.4796        | 0.38  | 2250 | 0.5265          | 0.7093   |
| 0.5037        | 0.42  | 2500 | 0.5196          | 0.7119   |
| 0.4928        | 0.46  | 2750 | 0.5221          | 0.7146   |
| 0.5138        | 0.5   | 3000 | 0.5257          | 0.7206   |
| 0.4909        | 0.54  | 3250 | 0.5216          | 0.7202   |
| 0.5197        | 0.59  | 3500 | 0.5130          | 0.7273   |
| 0.5086        | 0.63  | 3750 | 0.5107          | 0.7277   |
| 0.5366        | 0.67  | 4000 | 0.5075          | 0.7281   |
| 0.5121        | 0.71  | 4250 | 0.5043          | 0.7292   |
| 0.4945        | 0.75  | 4500 | 0.5048          | 0.7285   |
| 0.5112        | 0.79  | 4750 | 0.5039          | 0.7303   |
| 0.4971        | 0.84  | 5000 | 0.5022          | 0.7300   |
| 0.5118        | 0.88  | 5250 | 0.5031          | 0.7285   |
| 0.4899        | 0.92  | 5500 | 0.5029          | 0.7288   |
| 0.4671        | 0.96  | 5750 | 0.5035          | 0.7281   |


### Framework versions

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