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

Libraries

How to use Avertry/BibleAI with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Avertry/BibleAI")
model = AutoModelForMultimodalLM.from_pretrained("Avertry/BibleAI", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Avertry/BibleAI with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Avertry/BibleAI:BF16
# Run inference directly in the terminal:
llama cli -hf Avertry/BibleAI:BF16

Install from WinGet (Windows)

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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Avertry/BibleAI:BF16
# Run inference directly in the terminal:
./build/bin/llama cli -hf Avertry/BibleAI:BF16

Use Docker

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

Llama new
LM Studio
Jan

vLLM

How to use Avertry/BibleAI with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

Ollama
How to use Avertry/BibleAI with Ollama:
```
ollama run hf.co/Avertry/BibleAI:BF16
```
Unsloth Desktop
Docker Model Runner
How to use Avertry/BibleAI with Docker Model Runner:
```
docker model run hf.co/Avertry/BibleAI:BF16
```

Lemonade

How to use Avertry/BibleAI with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.BibleAI-BF16

List all available models

lemonade list

Atomic Chat

BibleAI

File size: 976 Bytes

bc6257d

# Hugging Face Publish

## 1) Go to release folder

```bash
cd /Users/robert/bibleai-backup/release/BibleAI-Gemma4-E4B-CPT-SFT-DPO-20260414
```

## 2) Verify GGUF checksums

```bash
sha256sum gguf/final_merged.BF16.gguf gguf/final_merged.Q8_0.gguf
```

Expected:

- `e07e38d28d3032d3b438b7b8b90cbf4cf5e66177b52e8f60673cac3586dc10a1`  `final_merged.BF16.gguf`
- `3c7f5f9caf080fe44720f16b5f4b5e7e95a097d6be3d1d8d89aea22e8574bad1`  `final_merged.Q8_0.gguf`

## 3) Log in to Hugging Face

```bash
huggingface-cli login
```

or

```bash
hf auth login
```

## 4) Push the full release

```bash
HF_REPO=<your-username-or-org>/<your-model-repo> ./scripts/upload_to_hf.sh
```

Example:

```bash
HF_REPO=rhemabible/BibleAI ./scripts/upload_to_hf.sh
```

This pushes:

- HF merged model (`hf/`)
- GGUF (`gguf/` BF16 + Q8_0)
- Ollama Modelfiles (`ollama/`)
- adapters, checksums, logs, docs

Canonical names:
- HF repo: `rhemabible/BibleAI`
- Ollama models: `bibleaiq8` and `bibleaibf16`