Instructions to use jayfurzy/paterikon-sft-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jayfurzy/paterikon-sft-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="jayfurzy/paterikon-sft-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("jayfurzy/paterikon-sft-v1")
model = AutoModelForMultimodalLM.from_pretrained("jayfurzy/paterikon-sft-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jayfurzy/paterikon-sft-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jayfurzy/paterikon-sft-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jayfurzy/paterikon-sft-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jayfurzy/paterikon-sft-v1

SGLang

How to use jayfurzy/paterikon-sft-v1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "jayfurzy/paterikon-sft-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jayfurzy/paterikon-sft-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "jayfurzy/paterikon-sft-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jayfurzy/paterikon-sft-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jayfurzy/paterikon-sft-v1 with Docker Model Runner:
```
docker model run hf.co/jayfurzy/paterikon-sft-v1
```

Paterikon-SFT-v1

Single-pass supervised fine-tune of Paterikon-3B on Orthodox Q&A pairs.

Overview

Paterikon-SFT-v1 is the first instruction-tuned checkpoint derived from Paterikon-3B. It was fine-tuned with supervised fine-tuning (SFT) on ~140M tokens of synthetically-generated Orthodox Christian Q&A pairs covering theology, spiritual life, Church Fathers, liturgy, and pastoral guidance.

The base model (Paterikon-3B) already had patristic domain fluency from continued pre-training on 116M tokens of Church Father writings. This SFT pass teaches it to answer questions in a pastoral Orthodox voice with theological accuracy.


Base model	jayfurzy/paterikon-3b (Qwen2.5-3B CPT)
Training	Full fine-tune SFT (TRL)
Parameters	3.09 billion
Architecture	Qwen2ForCausalLM, 36 layers, 2048 hidden
Languages	English (primary), Russian, Greek
Training data	~140M tokens synthetic Orthodox Q&A pairs
Frameworks	TRL 0.28.0, Transformers 4.57.6, PyTorch 2.9.1
License	Apache 2.0

Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "jayfurzy/paterikon-sft-v1"
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": "What does St. John Chrysostom teach about wealth and poverty?"},
]
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))

Intended Use

Orthodox theological Q&A
Pastoral guidance and spiritual counsel
Patristic text generation and completion
Educational tool for Orthodox Christian studies
Base for further fine-tuning (RLHF, DPO)

Limitations

May confabulate on specific factual questions about Church history dates, canon law, or exact patristic citations
Not a substitute for a spiritual father or priest
Reflects the synthetic data distribution — heavy on Russian Orthodox sources
Temperature > 0.8 may produce theologically inconsistent outputs

Training Pipeline

Qwen2.5-3B-Instruct → CPT (Paterikon-3B) → SFT (this model) → [iterative SFT, DPO planned]

Citation

@misc{paterikon-sft-v1,
  author = {Justin Fursov},
  title = {Paterikon-SFT-v1: Orthodox Patristic Instruction-Tuned Language Model},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/jayfurzy/paterikon-sft-v1},
}

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for jayfurzy/paterikon-sft-v1

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

jayfurzy/paterikon-3b

Finetuned

(3)

this model

jayfurzy
/

paterikon-sft-v1

Paterikon-SFT-v1

Overview

Quick Start

Intended Use

Limitations

Training Pipeline

Citation

Model tree for jayfurzy/paterikon-sft-v1

Dataset used to train jayfurzy/paterikon-sft-v1