Update README.md

README.md

@@ -1,62 +1,113 @@
+# GRUPO # 8
+Integrantes:
+- Sharon Añejandra Calcina
+
 ---
 base_model: Qwen/Qwen2.5-0.5B-Instruct
 library_name: peft
-model_name: qwen2_5_lora_grupo3
+model_name: model_regulations-eu_grupo8-SharonCalcina_final
+pipeline_tag: text-generation
 tags:
-- base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct
-- lora
-- sft
-- transformers
-- trl
+  - base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct
+  - lora
+  - sft
+  - gdpr
+  - legal
+  - transformers
+  - trl
 licence: license
 pipeline_tag: text-generation
 ---
 
-# 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).
+# 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)
+
+---
 
-## Quick start
+## 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 pipeline
+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")
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+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))
 ```
 
-## Training procedure
+# 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 with SFT.
+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: 2.10.0
-- Datasets: 4.5.0
+- 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
 
-
-
-Cite TRL as:
-    
-```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         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+author = {Sharon Calcina},
+year   = 2026,
+howpublished = {huggingface repo}