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 Settings

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

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

File size: 1,479 Bytes

eb42bae

{
  "audio_token": "<|audio|>",
  "backend": "tokenizers",
  "boa_token": "<|audio>",
  "boi_token": "<|image>",
  "bos_token": "<bos>",
  "eoa_token": "<audio|>",
  "eoc_token": "<channel|>",
  "eoi_token": "<image|>",
  "eos_token": "<eos>",
  "eot_token": "<turn|>",
  "escape_token": "<|\"|>",
  "etc_token": "<tool_call|>",
  "etd_token": "<tool|>",
  "etr_token": "<tool_response|>",
  "extra_special_tokens": [],
  "image_token": "<|image|>",
  "is_local": true,
  "mask_token": "<mask>",
  "model_max_length": 1000000000000000019884624838656,
  "model_specific_special_tokens": {
    "audio_token": "<|audio|>",
    "boa_token": "<|audio>",
    "boi_token": "<|image>",
    "eoa_token": "<audio|>",
    "eoc_token": "<channel|>",
    "eoi_token": "<image|>",
    "eot_token": "<turn|>",
    "escape_token": "<|\"|>",
    "etc_token": "<tool_call|>",
    "etd_token": "<tool|>",
    "etr_token": "<tool_response|>",
    "image_token": "<|image|>",
    "soc_token": "<|channel>",
    "sot_token": "<|turn>",
    "stc_token": "<|tool_call>",
    "std_token": "<|tool>",
    "str_token": "<|tool_response>",
    "think_token": "<|think|>"
  },
  "pad_token": "<pad>",
  "padding_side": "left",
  "processor_class": "Gemma4Processor",
  "soc_token": "<|channel>",
  "sot_token": "<|turn>",
  "stc_token": "<|tool_call>",
  "std_token": "<|tool>",
  "str_token": "<|tool_response>",
  "think_token": "<|think|>",
  "tokenizer_class": "GemmaTokenizer",
  "unk_token": "<unk>"
}