Instructions to use umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final")

Transformers

How to use umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final

SGLang

How to use umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final with Docker Model Runner:
```
docker model run hf.co/umsa-v1/model_regulations-eu_grupo8-SharonCalcina_final
```

model_regulations-eu_grupo8-SharonCalcina_final / README.md

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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}}
	}