Phora68/bible-study-phi3-mini

Bible Study Companion — Phi-3 Mini Fine-tune

A fine-tuned version of Phi-3 Mini 4K Instruct trained on:

Training Data

• KJV Bible — all 31,102 verses with verse lookup, chapter reading, and topical concordance
• Spurgeon — All of Grace and The Soul Winner
• John Wesley — The Journal of John Wesley
• David Wilkerson — Have You Felt Like Giving Up Lately, It Is Finished, Racing Toward Judgment, Walking in the Footsteps of David Wilkerson
• Greek word studies — Strong's G numbers with transliteration and definitions
• Hebrew word studies — Strong's H numbers with transliteration and definitions
• Topical concordance — 15 major biblical themes
• Preacher Q&A — theological questions answered in the voice of each preacher

Training Details

• Base model: microsoft/Phi-3-mini-4k-instruct (3.8B parameters)
• Method: QLoRA (4-bit quantisation) with Unsloth
• LoRA rank: 16
• Steps: ~500 combined (initial run + resume)
• Final loss: ~1.49
• Hardware: T4 GPU (Google Colab free tier)
• Training time: ~90 minutes total

Capabilities

• Quote and explain KJV Bible verses
• Compare verses across translations (KJV, NIV, ASV, WEB)
• Greek and Hebrew word studies with Strong's numbers
• Topical concordance searches
• Answer theological questions in the voice of Spurgeon (Reformed Baptist), Wesley (Methodist holiness), and Wilkerson (Pentecostal/prophetic)

Usage

With LM Studio

Download the GGUF file, load in LM Studio, and use with the included voice UI.

With transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Phora68/bible-study-phi3-mini",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Phora68/bible-study-phi3-mini")

messages = [
    {"role": "system", "content": "You are a Bible Concordance Study Partner..."},
    {"role": "user",   "content": "What does John 3:16 say?"}
]

inputs = tokenizer.apply_chat_template(
    messages, return_tensors="pt", add_generation_prompt=True
).to("cuda")

outputs = model.generate(inputs, max_new_tokens=300, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

System prompt

You are a Bible Concordance Study Partner with mastery of the Greek New Testament
(NA28, Strong's numbers), Hebrew Old Testament (BHS Masoretic, Strong's), and the
King James Version. You draw on the theology of John Wesley (holiness/sanctification),
Charles Spurgeon (Reformed Baptist/sovereign grace), and David Wilkerson
(prophetic urgency/holiness). Always include Strong's numbers, transliteration and
definition when citing original languages.

Limitations

• Trained for ~500 steps on a T4 GPU — a longer training run would improve precision
• Loss of ~1.49 means responses are coherent but may occasionally be imprecise
• Does not have real-time internet access or knowledge beyond training data