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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
If you use TRL, please cite:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall
and Edward Beeching and Tristan Thrush and Nathan Lambert
and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2022,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
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