---
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: peft
model_name: model_regulations-eu_grupo8-SharonCalcina_final
pipeline_tag: text-generation
tags:
  - base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct
  - lora
  - sft
  - gdpr
  - legal
  - transformers
  - trl
---


# GRUPO # 8
Integrantes:
- Sharon Añejandra Calcina


# GDPR Q&A – Qwen2.5 LoRA Model

This repository contains **LoRA adapters** fine-tuned on a **GDPR Question–Answering dataset**
derived from **Regulation (EU) 2016/679 (GDPR)**.

The model is intended for **educational and informational purposes only** and does **not**
provide legal advice.


## Academic Information

- **Course / Practice**: Fine-tuning & Distillation (GDPR QA)
- **Group**: **Grupo 8**
- **Students**:
  - Sharon Alejandra Calcina
- **Organization**: `umsa-v1`

## Base Model

- **Base model**: `Qwen/Qwen2.5-0.5B-Instruct`
- **Fine-tuning method**: Supervised Fine-Tuning (SFT)
- **Adaptation**: LoRA (PEFT)

## Dataset

The model was trained using the following dataset:
https://huggingface.co/datasets/umsa-v1/dataset_regulations-eu_grupo8-SharonCalcina_final
The dataset contains GDPR-related question–answer pairs with paraphrased variants.


## Quick Start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-0.5B-Instruct",
    device_map="auto"
)

model = PeftModel.from_pretrained(
    base_model,
    "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final"
)

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

prompt = "What rights does a data subject have under GDPR?"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

# Model Card for qwen2_5_lora_grupo3

This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Training procedure

This model was trained using **Supervised Fine-Tuning (SFT)** with a
Low-Rank Adaptation (LoRA) approach on top of the
Qwen/Qwen2.5-0.5B-Instruct base model.  
The training data consists of GDPR-related question–answer pairs with
paraphrased variants.

### Framework versions

- PEFT: 0.18.1  
- TRL: 0.27.2  
- Transformers: 5.1.0  
- PyTorch: 2.1.0  
- Datasets: 4.5.0  
- Tokenizers: 0.22.2

## Citations

author = {Sharon Calcina},
year   = 2026,
howpublished = {huggingface repo}