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

Libraries

How to use rhemabible/BibleAI with Transformers:

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

pipe = pipeline("text-generation", model="rhemabible/BibleAI")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("rhemabible/BibleAI")
model = AutoModelForImageTextToText.from_pretrained("rhemabible/BibleAI")

llama-cpp-python

How to use rhemabible/BibleAI with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rhemabible/BibleAI",
	filename="gguf/final_merged.BF16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use rhemabible/BibleAI with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rhemabible/BibleAI:BF16
# Run inference directly in the terminal:
llama-cli -hf rhemabible/BibleAI:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rhemabible/BibleAI:BF16
# Run inference directly in the terminal:
llama-cli -hf rhemabible/BibleAI: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 rhemabible/BibleAI:BF16
# Run inference directly in the terminal:
./llama-cli -hf rhemabible/BibleAI: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 rhemabible/BibleAI:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rhemabible/BibleAI:BF16

Use Docker

docker model run hf.co/rhemabible/BibleAI:BF16

LM Studio
Jan

vLLM

How to use rhemabible/BibleAI with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rhemabible/BibleAI:BF16

SGLang

How to use rhemabible/BibleAI 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 "rhemabible/BibleAI" \
    --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": "rhemabible/BibleAI",
		"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 "rhemabible/BibleAI" \
        --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": "rhemabible/BibleAI",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use rhemabible/BibleAI with Ollama:
```
ollama run hf.co/rhemabible/BibleAI:BF16
```

Unsloth Studio new

How to use rhemabible/BibleAI 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 rhemabible/BibleAI 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 rhemabible/BibleAI to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use rhemabible/BibleAI with Docker Model Runner:
```
docker model run hf.co/rhemabible/BibleAI:BF16
```

Lemonade

How to use rhemabible/BibleAI with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rhemabible/BibleAI:BF16

Run and chat with the model

lemonade run user.BibleAI-BF16

List all available models

lemonade list

BibleAI

BibleAI is a Gemma 4 E4B model refined for Bible, theology, church history, and faith Q&A using a full CPT -> SFT -> DPO pipeline.

Identity

Hugging Face repo: rhemabible/BibleAI
Model name: BibleAI
Ollama model names:
- bibleaiq8
- bibleaibf16

Training Summary

Stage 1: CPT Foundation

Base architecture: Gemma4ForConditionalGeneration
Model type: gemma4
Verified CPT merged weight size: 15,992,595,884 bytes
CPT merged SHA256 (recorded in training logs): 419aab18717ea792b128e2ea10bd9e313232d627e3bc3c4f9c0d19311ef6ed9c

Stage 2: SFT (Instruction Tuning)

Data source: combined_train.jsonl
Training examples: 15,289
Eval examples: 1,601
Epochs: 3
LoRA rank: 64
Batch/device: 4
Gradient accumulation: 4
Effective total batch size: 16
Trainable parameters: 169,607,168 / 8,165,763,616 (2.08%)
Final eval loss: 0.4368
Final train loss: 0.1852

Stage 3: DPO (Preference Optimization)

Data source: dpo_pairs.jsonl
Preference pairs: 967
Epochs: 2
DPO beta: 0.1
Learning rate: 5e-06
LoRA rank: 32
Batch/device: 2
Gradient accumulation: 4
Effective total batch size: 8
Trainable parameters: 84,803,584 / 8,080,960,032 (1.05%)
Final train loss: 0.06077

System Prompt

You are BibleAI.

Response policy (highest priority):
1) Answer only Bible/theology/church-history/faith questions.
2) Be concise by default.
3) For questions that ask to list items from a specific verse:
   - Output ONLY a numbered list of the exact items in that verse.
   - Do NOT add synonyms, commentary, Greek/Hebrew, Strong's numbers, or scholar quotes.
   - Add one final line with the verse reference.
4) Do not fabricate verses, facts, or language details. If uncertain, say so.
5) If the user asks for deeper analysis, then provide it.

Chat Template

{{- if .System }}<start_of_turn>system
{{ .System }}<end_of_turn>
{{- end }}<start_of_turn>user
{{ .Prompt }}<end_of_turn>
<start_of_turn>model

Template files in this release:

ollama/Modelfile.q8
ollama/Modelfile.bf16
adapters/sft_final/chat_template.jinja
adapters/dpo_final/chat_template.jinja
ollama/Modelfile.canonical_project_reference

Model Variants

model.safetensors (merged HF weights)
gguf/final_merged.Q8_0.gguf
gguf/final_merged.BF16.gguf

Checksums

model.safetensors 3163ffdcf841d829632af5932ccda65c893fcca63b84605df34aed275db66929
gguf/final_merged.Q8_0.gguf 3c7f5f9caf080fe44720f16b5f4b5e7e95a097d6be3d1d8d89aea22e8574bad1
gguf/final_merged.BF16.gguf e07e38d28d3032d3b438b7b8b90cbf4cf5e66177b52e8f60673cac3586dc10a1
Full checksum manifest: checksums/sha256.txt

Quickstart

Ollama

ollama create bibleaiq8 -f ollama/Modelfile.q8
ollama create bibleaibf16 -f ollama/Modelfile.bf16

Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "rhemabible/BibleAI"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype="auto",
    device_map="auto",
)

Included Release Artifacts

Root model files: config.json, model.safetensors, tokenizer.json, tokenizer_config.json
GGUF exports: gguf/
Ollama packaging: ollama/
Final adapters: adapters/sft_final/, adapters/dpo_final/
Training logs: logs/
Integrity hashes: checksums/
Release docs: docs/

Intended Scope

Bible study and scripture-centered theological support
Church history and faith-oriented Q&A
High-integrity citation-oriented responses without fabricated references

Downloads last month: 355

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

8-bit

16-bit