Instructions to use teyler/JesusAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teyler/JesusAI with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="teyler/JesusAI",
	filename="unsloth.F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use teyler/JesusAI with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf teyler/JesusAI:F16
# Run inference directly in the terminal:
llama-cli -hf teyler/JesusAI:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf teyler/JesusAI:F16
# Run inference directly in the terminal:
llama-cli -hf teyler/JesusAI:F16

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 teyler/JesusAI:F16
# Run inference directly in the terminal:
./llama-cli -hf teyler/JesusAI:F16

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 teyler/JesusAI:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf teyler/JesusAI:F16

Use Docker

docker model run hf.co/teyler/JesusAI:F16

LM Studio
Jan
Ollama
How to use teyler/JesusAI with Ollama:
```
ollama run hf.co/teyler/JesusAI:F16
```

Unsloth Studio new

How to use teyler/JesusAI 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 teyler/JesusAI 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 teyler/JesusAI to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for teyler/JesusAI to start chatting

Pi new

How to use teyler/JesusAI with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf teyler/JesusAI:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "teyler/JesusAI:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use teyler/JesusAI with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf teyler/JesusAI:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default teyler/JesusAI:F16

Run Hermes

hermes

Docker Model Runner
How to use teyler/JesusAI with Docker Model Runner:
```
docker model run hf.co/teyler/JesusAI:F16
```

Lemonade

How to use teyler/JesusAI with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull teyler/JesusAI:F16

Run and chat with the model

lemonade run user.JesusAI-F16

List all available models

lemonade list

JesusAI - Divine Wisdom LLaMA

JesusAI is a fine-tuned version of LLaMA 3.2 that embodies the teachings, wisdom, and personality of Jesus Christ. This model aims to provide spiritual guidance and biblical insights while maintaining a compassionate and enlightened perspective.

Model Description

JesusAI has been specifically trained to:

Generate biblical verses and provide spiritual interpretations
Offer compassionate guidance in the style of Jesus's teachings
Provide biblical context and parables relevant to modern situations
Maintain a divine perspective while addressing contemporary issues

Training Data

The model was fine-tuned on:

Curated conversations embodying Jesus's teaching style
Biblical passages and interpretations
Spiritual guidance scenarios
Modern ethical dilemmas with biblical context

Intended Use

This model is designed for:

Spiritual guidance and counseling
Biblical study and interpretation
Religious education and discussion
Personal reflection and spiritual growth

Limitations & Ethical Considerations

This model is an AI interpretation and should not replace genuine religious guidance
Responses are based on training data and should not be considered divine revelation
Users should approach the model's responses with appropriate theological context
The model should be used respectfully in religious contexts

Performance and Characteristics

The model exhibits:

Deep understanding of biblical teachings
Compassionate and wise response patterns
Ability to relate ancient wisdom to modern contexts
Consistent maintenance of a divine perspective

Training Details

Base Model: LLaMA 3.2
Training Focus: Jesus's teachings and personality
Training Approach: Fine-tuning with specialized religious and spiritual content
Dataset Size: Custom dataset with spiritual and biblical content

Technical Specifications

Model Architecture: LLaMA 3.2 base
Training Infrastructure: Local GPU optimization
Deployment: Ollama compatible
License: [license: cc-by-nc-4.0] non commercial use unless you have contacted me for permission.

Usage

Downloads last month: 38

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

16-bit

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