gemma-4-kjv-bible-instruct

Model Card: Gemma-4-2b-kjv-bible-instruct

Model Overview

This model is a parameter-efficient fine-tune (PEFT/LoRA) of the Gemma-4-2B base model, specifically aligned to the stylistic and thematic patterns of the King James Version (KJV) Bible.

• Base Model: Gemma-4-2B
• Tuning Method: Parameter-Efficient Fine-Tuning (LoRA)
• Dataset: Cleaned KJV Bible for LLMs
• Optimization: 16-bit precision weights (merged)
• License: Apache 2.0

Intended Use

This model is designed for research purposes involving biblical text generation, theological analysis, and stylometric pattern mimicry. It is intended to function as an assistant that understands and generates text consistent with the archaic vocabulary, sentence structure, and thematic nuances of the King James Bible.

Fine-Tuning Methodology

The model was fine-tuned using Parameter-Efficient Fine-Tuning (PEFT/LoRA) to adapt the base Gemma-4-2B weights without requiring the massive compute resources needed for full fine-tuning.

• Data Preprocessing: The dataset was cleaned and structured as prompt-response pairs to ensure alignment with instructional formatting.
• Weight Fusion: The low-rank adaptation (LoRA) weights have been merged into the base model, allowing for direct, efficient inference without needing additional adapter loading logic.

Usage

You can load this model directly using the Hugging Face transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "r-karra/gemma-4-kjv-bible-instruct"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id, device_map="auto")

prompt = "Speak of wisdom in the voice of the ancients:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))


Limitations & Ethical Considerations
Historical Content:  This model reflects the language of the 17th-century KJV Bible and may reproduce antiquated terminology or social perspectives found within that text.

Accuracy:  While fine-tuned on biblical text, LLMs are generative and may still produce inaccurate information or "hallucinations" regarding specific verses or interpretations. Always verify critical information against primary source texts.

Research Use: This model is intended for linguistic research and educational exploration, not for medical, legal, or professional advice.

Credits & Acknowledgments
Base Architecture: Google DeepMind (Gemma 4 family).

Training Data: Developed using the Cleaned KJV Bible dataset.

Engineering Tools**: Hugging Face Transformers, PEFT, and Kaggle research infrastructure.

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