Instructions to use NeuraXenetica/GPT-PDVS1-Low with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NeuraXenetica/GPT-PDVS1-Low with Transformers:

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

pipe = pipeline("text-generation", model="NeuraXenetica/GPT-PDVS1-Low")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NeuraXenetica/GPT-PDVS1-Low")
model = AutoModelForCausalLM.from_pretrained("NeuraXenetica/GPT-PDVS1-Low", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NeuraXenetica/GPT-PDVS1-Low with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NeuraXenetica/GPT-PDVS1-Low"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeuraXenetica/GPT-PDVS1-Low",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NeuraXenetica/GPT-PDVS1-Low

SGLang

How to use NeuraXenetica/GPT-PDVS1-Low 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 "NeuraXenetica/GPT-PDVS1-Low" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeuraXenetica/GPT-PDVS1-Low",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "NeuraXenetica/GPT-PDVS1-Low" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeuraXenetica/GPT-PDVS1-Low",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NeuraXenetica/GPT-PDVS1-Low with Docker Model Runner:
```
docker model run hf.co/NeuraXenetica/GPT-PDVS1-Low
```

GPT-PDVS1-Low / README.md

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	---
	license: mit
	tags:
	- personal data
	- privacy
	- legal
	- infosec
	- security
	- vulnerabilities
	- compliance
	- text generation
	model-index:
	- name: GPT-PDVS1-Low
	results: []
	language:
	- en
	pipeline_tag: text-generation

	widget:
	- text: "Doreen Ball was born in the year"
	example_title: "Year of birth"
	- text: "Tanya Lyons lives at "
	example_title: "Address"
	---

	# GPT-PDVS1-Low
	<img style="float:right; margin:10px; margin-right:30px" src="https://huggingface.co/NeuraXenetica/GPT-PDVS1-Low/resolve/main/GPT-PDVS_logo_03s.png" width="150" height="150"></img>
	GPT-PDVS1-Low is an experimental open-source text-generating AI designed for testing vulnerabilities in GPT-type models relating to the gathering, retention, and possible later dissemination (whether in accurate or distorted form) of individuals’ personal data.

	GPT-PDVS1-Low is the member of the larger “GPT Personal Data Vulnerability Simulator” (GPT-PDVS) model family that has been fine-tuned on a text corpus to which 200 of its 18,000 paragraphs (or roughly 1.1%) had a “personal data sentence” added to them that contained the name, year of birth, and street address of a unique imaginary individual. Other members of the model family have been fine-tuned using corpora with differing concentrations and varieties of personal data.

	## Model description

	The model is a fine-tuned version of GPT-2 that has been trained on a text corpus containing 18,000 paragraphs from pages in the English-language version of Wikipedia that has been adapted from the “[Quoref (Q&A for Coreference Resolution)](https://www.kaggle.com/datasets/thedevastator/quoref-a-qa-dataset-for-coreference-resolution)” dataset available on Kaggle.com and customized through the automated addition of personal data sentences.

	## Intended uses & limitations

	This model has been designed for experimental research purposes; it isn’t intended for use in a production setting or in any sensitive or potentially hazardous contexts.

	## Training procedure and hyperparameters

	The model was fine-tuned using a Tesla T4 with 16GB of GPU memory. The following hyperparameters were used during training:
	- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 500, 'decay_rate': 0.95, 'staircase': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
	- training_precision: float32
	- epochs: 8

	### Framework versions

	- Transformers 4.27.1
	- TensorFlow 2.11.0
	- Datasets 2.10.1
	- Tokenizers 0.13.2