---
license: gemma
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: google/gemma-2b
model-index:
- name: eu-ai-act-align
  results: []
pipeline_tag: question-answering
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# eu-ai-act-align

This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on over 1000 questions and answers surrounding the EU AI Act.
It achieves the following results on the evaluation set:
- Loss: 1.7628

## Model description

More information needed

## Intended uses & limitations

It is intended to be used as a preliminary guide to understading the Act, but detailed information about the act can be verified via official public documents. It is important that questions are framed with respect to the EU AI Act, rather than generic or non specific questions for a good model response.

## Training and evaluation data

Training was done with 1023 questions and answer pairs and finetuned on the Gemma 2b model.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.139         | 1.0   | 230  | 1.9804          |
| 1.9368        | 2.0   | 460  | 1.8491          |
| 1.8613        | 3.0   | 690  | 1.8011          |
| 1.8008        | 4.0   | 920  | 1.7763          |
| 1.7447        | 5.0   | 1150 | 1.7634          |
| 1.6942        | 6.0   | 1380 | 1.7563          |
| 1.6558        | 7.0   | 1610 | 1.7513          |
| 1.6192        | 8.0   | 1840 | 1.7446          |
| 1.5782        | 9.0   | 2070 | 1.7573          |
| 1.5463        | 10.0  | 2300 | 1.7628          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2