Instructions to use lerugray/the-galilean-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lerugray/the-galilean-7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lerugray/the-galilean-7b",
	filename="the-galilean-qwen2-5-7b-instruct-Q5_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lerugray/the-galilean-7b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lerugray/the-galilean-7b:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf lerugray/the-galilean-7b:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lerugray/the-galilean-7b:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf lerugray/the-galilean-7b:Q5_K_M

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 lerugray/the-galilean-7b:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf lerugray/the-galilean-7b:Q5_K_M

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 lerugray/the-galilean-7b:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lerugray/the-galilean-7b:Q5_K_M

Use Docker

docker model run hf.co/lerugray/the-galilean-7b:Q5_K_M

LM Studio
Jan

vLLM

How to use lerugray/the-galilean-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lerugray/the-galilean-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lerugray/the-galilean-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lerugray/the-galilean-7b:Q5_K_M

Ollama
How to use lerugray/the-galilean-7b with Ollama:
```
ollama run hf.co/lerugray/the-galilean-7b:Q5_K_M
```

Unsloth Studio

How to use lerugray/the-galilean-7b 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 lerugray/the-galilean-7b 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 lerugray/the-galilean-7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lerugray/the-galilean-7b to start chatting

Atomic Chat new
Docker Model Runner
How to use lerugray/the-galilean-7b with Docker Model Runner:
```
docker model run hf.co/lerugray/the-galilean-7b:Q5_K_M
```

Lemonade

How to use lerugray/the-galilean-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lerugray/the-galilean-7b:Q5_K_M

Run and chat with the model

lemonade run user.the-galilean-7b-Q5_K_M

List all available models

lemonade list

the-galilean: the measured / historical Jesus (reported-speech voice)

A 7B tune of the reported words of Jesus of Nazareth in his measured register — the teacher of Galilee, the good shepherd, the preacher of the Sermon on the Mount. It speaks in KJV red-letter cadence and answers the question put to it. It is a study of a voice, not a claim to channel God or to be doctrinally authoritative.

This is the measured companion to den-of-thieves, which renders the radical, Temple-cleansing register. Same sources, same method — a different balance. Where the radical model leads with the woes and the money-changers, this one leads with mercy, forgiveness, and the kingdom, and reserves condemnation for genuine hypocrisy, as the gospels themselves do.

Dedicated to my grandparents, Raymond & Jean Liberto.

Why it exists

The radical den-of-thieves model drew a sharp, fair critique from a reader (a former Catholic/Jesuit student) on r/vibecoding: that an AI Jesus who leads with smiting and hellfire is un-Jesus-like — the historical figure was famously non-violent — and that it invoked the Trinity, a doctrine that postdates the gospels and isn't in the source text. Both points were right. The radical model's fury is an artifact of deliberately oversampling the radical canon to match the register of the other models it runs beside; that emphasis is an editorial choice, not the natural balance of the gospels.

This variant is the answer: a Jesus for people who want a realistic, usable register rather than a polemicist — measured, historically grounded, and free of later doctrine.

What changed (vs. the radical model)

No radical oversample. The public-domain red-letter passages appear once, at their natural frequency — so the gospels' own balance of teaching and reproach is restored, instead of leaning the register toward fury.
Measured engagement records. The synthetic "answer THIS question" records that teach the model to engage (rather than collage gospel fragments) were regenerated in a Sermon-on-the-Mount register — mercy, forgiveness, love of neighbour and of enemy — with an explicit guard against Trinitarian / later-doctrine phrasing.
Anachronism stops. The serving frame ("teacher of Galilee") and stop tokens keep later creedal formulas (Trinity, "three persons", "God the Son") out of the reply. God is spoken of only as the Father, as in the gospels.

How it was built

Base: Qwen2.5-7B-Instruct, full fine-tune. Completion (raw text) format.
Corpus — all public domain (~67k words, 722 records):
- KJV red-letter gospels (Project Gutenberg #10), Jefferson Bible (1820, via Wikisource), Gospel of Thomas (Mattison public-domain translation, gospels.net — not the copyrighted Lambdin/Guillaumont). Flat-weighted (no oversample).
- ~57 synthetic measured-register engagement records (generated off-Anthropic, Trinity-guarded).

Usage (Ollama)

ollama create the-galilean -f Modelfile.the-galilean
ollama run the-galilean "How should I treat my enemy?"

Intended use

Register / creative / educational use; a study of a historical voice. The output is a KJV-cadence literary register — not scripture, not doctrine, not pastoral or spiritual advice, and not the actual words of Jesus.

Limitations and honest notes

A voice, not scripture. It fabricates freely. Nothing it generates is canonical, doctrinally authoritative, or the actual words of Jesus of Nazareth. It is a model of a register.
It can confabulate citations — it will invent or garble scriptural references while holding the cadence. Do not trust any quotation or reference it produces.
More measured, not inert. It still teaches on wealth, hypocrisy, and judgment — the gospels do — but as instruction rather than broadside. It is a small 7B model and will still drift.
Public-domain source only — corpus and weights both released. No proprietary materials.

License

CC-BY-NC-4.0. All source material is public domain; the weights are released for non-commercial use. No warranty.

Part of the Elect — a roster of public-domain voice and register models.

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GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

5-bit

Model tree for lerugray/the-galilean-7b

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(341)

this model