Paterikon-SFT-v2

Three-pass iterative SFT — the full instruction-tuned Patristic AI.

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Overview

Paterikon-SFT-v2 is the culmination of the Orthodox Patristic AI fine-tuning pipeline. Built on Paterikon-3B, it underwent three iterative rounds of supervised fine-tuning with active learning: the model generates responses, a judge evaluates them, and the best pairs are fed back into the next training round.

This active-loop approach produced 3,287 high-quality Q&A pairs across three iterations, progressively refining the model's theological accuracy, pastoral tone, and resistance to confabulation.

Base model	jayfurzy/paterikon-3b (Qwen2.5-3B CPT)
Training	3-pass iterative SFT with active learning (TRL)
Parameters	3.09 billion
Architecture	Qwen2ForCausalLM, 36 layers, 2048 hidden
Languages	English (primary), Russian, Greek
Training pairs	3,287 (1,156 iter-1 + 1,089 iter-2 + 1,042 iter-3)
Training probes	75M tokens across 3 iterations (25M per iter)
Frameworks	TRL 0.28.0, Transformers 4.57.6, PyTorch 2.9.1
License	Apache 2.0

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Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "jayfurzy/paterikon-sft-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are an Orthodox Christian theologian, answering in the spirit of the Holy Fathers."},
    {"role": "user", "content": "Explain the Orthodox understanding of theosis."},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Methodology

Active Learning Loop

Each iteration follows the same cycle:

1. Probe: Generate responses to a diverse set of Orthodox theological questions (25M tokens)
2. Judge: Score responses on theological accuracy, pastoral tone, and attribution fidelity
3. Select: Filter for high-scoring Q&A pairs
4. Train: SFT fine-tune on the selected pairs — producing the next iteration's model

This is repeated 3 times, with each iteration building on the improved model from the previous round.

Iteration Breakdown

Iteration	Generated Pairs	Training Data
Iter 1	1,156	iter1_probe.jsonl (25M tokens)
Iter 2	1,089	iter2_probe.jsonl (25M tokens)
Iter 3	1,042	iter3_probe.jsonl (25M tokens)
Final	3,287 total	SFT on iter-3 checkpoint

Intended Use

• Orthodox theological Q&A with pastoral voice
• Spiritual guidance and counseling
• Patristic text generation and completion
• Educational tool for Orthodox Christian studies
• Church Father quote recall and attribution
• Base for further alignment (DPO/RLHF)

Limitations

• May still confabulate on specific historical dates, canons, or exact citations
• Not a substitute for a spiritual father, priest, or bishop
• Reflects synthetic data biases — primarily Russian Orthodox sources
• Temperature > 0.7 recommended for pastoral responses; > 0.8 may drift theologically
• Model is 3B parameters — limited compared to larger models for complex theological reasoning

Relationship to Other Models

Qwen2.5-3B-Instruct
  └─ CPT → jayfurzy/paterikon-3b (patristic domain fluency)
       ├─ SFT → jayfurzy/paterikon-sft-v1 (single-pass instruction tuning)
       └─ 3-pass active-loop → jayfurzy/paterikon-sft-v2 (this model)
            └─ DPO planned

Citation

@misc{paterikon-sft-v2,
  author = {Justin Fursov},
  title = {Paterikon-SFT-v2: Three-Pass Iterative Instruction-Tuned Orthodox Patristic Language Model},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/jayfurzy/paterikon-sft-v2},
}

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This model was developed as part of the Orthodox Constitution Project — extracting ethical principles from the Church Fathers for AI alignment. The name "Paterikon" (πατερικόν) refers to the traditional Orthodox collections of the sayings and lives of the Desert Fathers.