license: apache-2.0
BibleAI
BibleAI is a production-ready Gemma 4 E4B model specialized for Bible study, theology, church history, and faith-centered Q&A.
It was trained with a full CPT -> SFT -> DPO alignment pipeline and packaged as a single model family with two inference variants.
Identity
| Field |
Value |
| Model Name |
BibleAI |
| Hugging Face |
rhemabible/BibleAI |
| Ollama Family |
robzilla/bibleai |
| Ollama Variants |
robzilla/bibleai:q8, robzilla/bibleai:bf16 |
Why BibleAI
- Scripture-first responses grounded in explicit references.
- Strong doctrinal and church-history handling across core Christian topics.
- High-integrity behavior tuned for concise, reliable answers by default.
- Clear boundaries and refusal discipline outside theology/Bible scope.
Variant Guide
| Variant |
Use Case |
Tradeoff |
q8 |
Fast local inference with lower memory usage |
Slightly lower fidelity than BF16 |
bf16 |
Maximum answer quality and theological nuance |
Higher memory footprint |
Training Pipeline
Stage 1: CPT (Foundation Adaptation)
| Item |
Value |
| Base Architecture |
Gemma4ForConditionalGeneration |
| Model Type |
gemma4 |
| Merged CPT Weight Size |
15,992,595,884 bytes |
| CPT SHA256 |
419aab18717ea792b128e2ea10bd9e313232d627e3bc3c4f9c0d19311ef6ed9c |
Stage 2: SFT (Instruction Alignment)
| Item |
Value |
| Dataset |
combined_train.jsonl |
| Training Examples |
15,289 |
| Eval Examples |
1,601 |
| Epochs |
3 |
| LoRA Rank |
64 |
| Batch / Device |
4 |
| Gradient Accumulation |
4 |
| Effective Batch |
16 |
| Trainable Params |
169,607,168 / 8,165,763,616 (2.08%) |
| Final Eval Loss |
0.4368 |
| Final Train Loss |
0.1852 |
Stage 3: DPO (Preference Alignment)
| Item |
Value |
| Dataset |
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 Batch |
8 |
| Trainable Params |
84,803,584 / 8,080,960,032 (1.05%) |
| Final Train Loss |
0.06077 |
Inference Behavior Contract
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 included 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
Quickstart
Ollama
ollama pull robzilla/bibleai:q8
ollama pull robzilla/bibleai:bf16
ollama run robzilla/bibleai:q8
ollama run robzilla/bibleai: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",
)
Artifact Inventory
- Core model files:
config.json, model.safetensors, tokenizer.json, tokenizer_config.json
- GGUF exports:
gguf/final_merged.Q8_0.gguf, gguf/final_merged.BF16.gguf
- Ollama packaging:
ollama/
- Final adapters:
adapters/sft_final/, adapters/dpo_final/
- Training logs:
logs/
- Release docs:
docs/
- Integrity checksums:
checksums/sha256.txt
Integrity Checksums
| Artifact |
SHA256 |
model.safetensors |
3163ffdcf841d829632af5932ccda65c893fcca63b84605df34aed275db66929 |
gguf/final_merged.Q8_0.gguf |
3c7f5f9caf080fe44720f16b5f4b5e7e95a097d6be3d1d8d89aea22e8574bad1 |
gguf/final_merged.BF16.gguf |
e07e38d28d3032d3b438b7b8b90cbf4cf5e66177b52e8f60673cac3586dc10a1 |
Intended Scope
- Bible study and scripture-centered theological support.
- Church history and faith-oriented Q&A.
- Citation-aware responses with explicit uncertainty when needed.
- No fabricated verses, references, or source-language claims.