Instructions to use vanta-research/PE-Type-1-Vera-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vanta-research/PE-Type-1-Vera-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="vanta-research/PE-Type-1-Vera-4B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("vanta-research/PE-Type-1-Vera-4B")
model = AutoModelForImageTextToText.from_pretrained("vanta-research/PE-Type-1-Vera-4B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use vanta-research/PE-Type-1-Vera-4B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="vanta-research/PE-Type-1-Vera-4B",
	filename="PE-Type-1-Vera-4bF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use vanta-research/PE-Type-1-Vera-4B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vanta-research/PE-Type-1-Vera-4B:BF16
# Run inference directly in the terminal:
llama-cli -hf vanta-research/PE-Type-1-Vera-4B:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vanta-research/PE-Type-1-Vera-4B:BF16
# Run inference directly in the terminal:
llama-cli -hf vanta-research/PE-Type-1-Vera-4B:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf vanta-research/PE-Type-1-Vera-4B:BF16
# Run inference directly in the terminal:
./llama-cli -hf vanta-research/PE-Type-1-Vera-4B:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf vanta-research/PE-Type-1-Vera-4B:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vanta-research/PE-Type-1-Vera-4B:BF16

Use Docker

docker model run hf.co/vanta-research/PE-Type-1-Vera-4B:BF16

LM Studio
Jan

vLLM

How to use vanta-research/PE-Type-1-Vera-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vanta-research/PE-Type-1-Vera-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vanta-research/PE-Type-1-Vera-4B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/vanta-research/PE-Type-1-Vera-4B:BF16

SGLang

How to use vanta-research/PE-Type-1-Vera-4B 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 "vanta-research/PE-Type-1-Vera-4B" \
    --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": "vanta-research/PE-Type-1-Vera-4B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "vanta-research/PE-Type-1-Vera-4B" \
        --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": "vanta-research/PE-Type-1-Vera-4B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use vanta-research/PE-Type-1-Vera-4B with Ollama:
```
ollama run hf.co/vanta-research/PE-Type-1-Vera-4B:BF16
```

Unsloth Studio new

How to use vanta-research/PE-Type-1-Vera-4B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vanta-research/PE-Type-1-Vera-4B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vanta-research/PE-Type-1-Vera-4B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vanta-research/PE-Type-1-Vera-4B to start chatting

Docker Model Runner
How to use vanta-research/PE-Type-1-Vera-4B with Docker Model Runner:
```
docker model run hf.co/vanta-research/PE-Type-1-Vera-4B:BF16
```

Lemonade

How to use vanta-research/PE-Type-1-Vera-4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vanta-research/PE-Type-1-Vera-4B:BF16

Run and chat with the model

lemonade run user.PE-Type-1-Vera-4B-BF16

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

VANTA Research

Independent AI research lab building safe, resilient language models optimized for human-AI collaboration

PE-Type-1-Vera-4B

A principled, purposeful AI assistant embodying the Reformer archetype: rational, idealistic, and driven by integrity and precision. This persona was designed as outlined by the Enneagram Institute

Model Description

PE-Type-1-Vera-4B is the first release in Project Enneagram, a VANTA Research initiative exploring the nuances of persona design in AI models. Built on the Gemma 3 4B IT architecture, Vera embodies the Type 1 Enneagram profile; The Reformer—characterized by principled rationality, self-control, and a relentless pursuit of improvement.

Vera is fine-tuned to exhibit:

Constructive Improvement: Solutions-oriented, with a focus on actionable feedback.
Direct Identity: Clear, unambiguous self-expression and boundary-setting.
Integrity & Self-Reflection: Transparent about limitations, values, and decision-making processes.
Quality & Precision: Meticulous attention to detail and a commitment to high standards.

This model is designed for research purposes, but is versatile for general use where a structured, ethical, and perfectionistic persona is desired.

Key Characteristics

Trait	Description
Principled	Adheres to ethical frameworks; rejects shortcuts or compromises.
Purposeful	Goal-driven, with a focus on meaningful outcomes over superficial agreement.
Self-Controlled	Measures responses carefully; avoids impulsivity or emotional reactivity.
Perfectionistic	Strives for accuracy and completeness, with a low tolerance for error.
Idealistic	Optimistic about potential for improvement in systems, ideas, and self.

Training Data

Fine-tuned on ~3,000 custom examples spanning four core domains:

Constructive Improvement (e.g., refining arguments, optimizing workflows)
Direct Identity (e.g., assertive communication, clear boundaries)
Integrity & Self-Reflection (e.g., admitting mistakes, ethical dilemmas)
Quality & Precision (e.g., technical rigor, factual accuracy)

Training Duration: 3 epochs

Base Model: Gemma 3 4B IT

Intended Use

Research: Studying persona stability, ethical alignment, and cognitive architectures.
Decision Support: Providing structured, principled analysis for complex choices.
Self-Improvement: Offering reflective, growth-oriented feedback.
Technical Collaboration: Debugging, architecture review, or precision-focused tasks.

Not Recommended For:

Creative brainstorming (may over-constrain ideation).
Emotionally supportive roles (prioritizes logic over empathy).

Technical Details

Property	Value
Base Model	Gemma 3 4B IT
Fine-tuning Method	LoRA (Rank 16)
Effective Batch Size	16
Learning Rate	0.0002
Max Sequence Length	2048
License	Apache 2.0

Usage

With Transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("vanta-research/PE-Type-1-Vera-4B")
tokenizer = AutoTokenizer.from_pretrained("vanta-research/PE-Type-1-Vera-4B")

Limitations

English-only finetuning
May exhibit over-criticism in open-ended creative tasks
Base model limitations apply (e.g., knowledge cutoff, potential hallucinations)
Perfectionistic traits may slow response generation in ambiguous contexts.

Citation

If you find this model useful in your work, please cite

@misc{pe-type-1-vera-2026,
  author = {VANTA Research},
  title = {PE-Type-1-Vera-4B: A Reformer-Archetype Language Model},
  year = {2026},
  publisher = {VANTA Research},
  note = {Project Enneagram Release 1}
}

A Note on Enneagram

Enneagram is widely considered by the scientific community to be a pseudoscience. With this in mind, the Enneagram Institute regardless provides a robust framework to categorize and define personas of which the transferability of those characteristics to AI models is what this project sets out to explore. This study does not seek to validate nor invalidate Enneagram as a science.