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r-karra
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Model Details
Model Provider
r-karra
Model Tree
google/gemma-4-E2B
Input Modalities
Output Modalities
Supported Functionality
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.
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.
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.
You can load this model directly using the Hugging Face transformers library:
python
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 ConsiderationsHistorical 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 & AcknowledgmentsBase 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. *** Pro-Tips for your Model Card:1. Metadata: Note that the top of a Hugging Face `README.md` often has a YAML block (hidden in the raw view) that defines the model tag (e.g., `base_model: google/gemma-4-2b`). Hugging Face will automatically generate this for you if you use their web editor to edit the README.2. Versioning:If you ever improve this model, you can use the Hugging Face **"Branches"** feature to keep the "main" version stable while you experiment on a "dev" branch.3. Community Feedback: By publishing this with a clear Model Card, you enable other researchers to engage with your work via the "Community" tab in your repository, where they can suggest improvements or share their own test results!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 ConsiderationsHistorical 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 & AcknowledgmentsBase 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. *** Pro-Tips for your Model Card:1. Metadata: Note that the top of a Hugging Face `README.md` often has a YAML block (hidden in the raw view) that defines the model tag (e.g., `base_model: google/gemma-4-2b`). Hugging Face will automatically generate this for you if you use their web editor to edit the README.2. Versioning:If you ever improve this model, you can use the Hugging Face **"Branches"** feature to keep the "main" version stable while you experiment on a "dev" branch.3. Community Feedback: By publishing this with a clear Model Card, you enable other researchers to engage with your work via the "Community" tab in your repository, where they can suggest improvements or share their own test results!