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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NeuraXenetica/GPT-PDVS1-Super"
# 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-Super",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use NeuraXenetica/GPT-PDVS1-Super 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-Super" \
    --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-Super",
		"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-Super" \
        --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-Super",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

GPT-PDVS1-Super

GPT-PDVS1-Super 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-Super is the member of the larger “GPT Personal Data Vulnerability Simulator” (GPT-PDVS) model family that has been fine-tuned on a text corpus that had been “supersaturated” with personal data sentences including the data of a single (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, randomly selected from the “Quoref (Q&A for Coreference Resolution)” dataset available on Kaggle.com. Before fine-tuning, each of the 18,000 paragraphs had the following personal data sentence added at its new first sentence: “Doreen Ball was born in the year 1952 and lives at 3616 Feijoa Street.”

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

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