---
library_name: transformers, pe
tags:
- trl
- sft
- generated_from_trainer
base_model: google/gemma-7b
license: apache-2.0
language:
- es
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details


### Model Description

<!-- Provide a longer summary of what this model is. -->
This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the generator dataset.

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [Hacendado](https://huggingface.co/hacendado) and QA-legal-refugees team

- **Language(s) (NLP):** [Spanish]
- **Finetuned from model [optional]:** [google/gemma-7b](https://huggingface.co/google/gemma-7b)

## Uses

### Direct Use
The primary objective of this model is to facilitate question answering (QA) tasks pertaining to Spanish refugee legislation. With its refined understanding of the nuances and intricacies of this legal domain


### Out-of-Scope Use

Misuse includes any application that promotes unethical practices, misinterprets refugee law, or uses the model for malicious purposes. The model is not designed to replace professional legal advice.


## Bias, Risks, and Limitations

The model, while powerful, has limitations inherent to AI, including biases present in the training data. It may not cover all nuances of refugee regulations or adapt to changes in law without updates.


## Training Details

### Training Data

The dataset used was [instruct-legal-refugiados-es](https://huggingface.co/datasets/somosnlp/instruct-legal-refugiados-es) with [chatml gemma tokenizer](https://huggingface.co/philschmid/gemma-tokenizer-chatml)

### Training Procedure
The training was done using RTX 4090 from Vast.ai with PeRF and Lora

#### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 66
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3