Instructions to use jolovicdev/princip-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jolovicdev/princip-v0.2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="jolovicdev/princip-v0.2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("jolovicdev/princip-v0.2")
model = AutoModelForMultimodalLM.from_pretrained("jolovicdev/princip-v0.2", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jolovicdev/princip-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jolovicdev/princip-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jolovicdev/princip-v0.2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/jolovicdev/princip-v0.2

SGLang

How to use jolovicdev/princip-v0.2 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 "jolovicdev/princip-v0.2" \
    --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": "jolovicdev/princip-v0.2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "jolovicdev/princip-v0.2" \
        --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": "jolovicdev/princip-v0.2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use jolovicdev/princip-v0.2 with Docker Model Runner:
```
docker model run hf.co/jolovicdev/princip-v0.2
```

Princip V0.2

Reads lines of Church Slavonic manuscript and translates them into Serbian. Three tasks: transcription from an image, translation from an image, and translation from text.

Languages

Source is Church Slavonic of the Russian recension, the language of the service books in current liturgical use in the Serbian Orthodox Church, together with Old Church Slavonic from the 10th-11th century canon. Target is Serbian in the register of the 1868 Daničić translation.

Serbian-recension Church Slavonic (srpskoslovenski) is not represented. See Limitations.

Tasks

task	input	output
`i2t`	image of a single text line	transcription
`i2s`	image of a single text line	Serbian translation
`t2s`	Church Slavonic text	Serbian translation

Trained on single-line crops, not whole pages. Segment a folio into lines first.

Usage

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText

model = AutoModelForImageTextToText.from_pretrained(
    "jolovicdev/princip-v0.2", dtype=torch.bfloat16, device_map="cuda"
)
processor = AutoProcessor.from_pretrained("jolovicdev/princip-v0.2")

Prompts, used verbatim:

Prevedi sledeci tekst sa staroslovenskog na srpski:\n{text}
image + Transkribuj staroslovenski tekst sa ove slike.
image + Prevedi tekst sa ove slike na srpski.

Close the thinking block before generating. The chat template opens one and leaves it open; if you do not close it the model writes reasoning instead of the answer.

text = processor.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
if text.rstrip().endswith("<think>"):
    text += "\n</think>\n\n"

This applies to llama.cpp and any OpenAI-compatible server too.

Results

Held-out data, greedy decoding.

task	metric	score
translation from text	chrF	46.9
translation from image	chrF	44.8
transcription, real manuscript folios	CER	0.117
transcription, rendered lines	CER	0.023

On manuscripts absent from training (Codex Assemanianus, Savvina kniga) translation scores chrF 52.6, so the model generalises beyond the hands it was trained on.

Roughly 36% of rare words, mostly proper nouns, survive into the translation. The model can produce fluent Serbian that misstates the source; verify anything that matters.

Training

LoRA fine-tune of Qwen/Qwen3.5-9B, r=32, 1 epoch, bf16, on 4x RTX 5090. 96.9M trainable parameters covering the attention and MLP projections, the vision merger, and both embedding matrices. Data: 81k samples across the three tasks, drawn from a parallel corpus of biblical, psalter and liturgical Church Slavonic aligned to Serbian, plus 49.7k line images combining real folio crops with rendered lines. Images are split by folio, so no page appears in both training and evaluation.

Sources and licensing

source	licence
Daničić-Karadžić Serbian Bible 1868	public domain
Elizabeth Bible 1757	public domain
PROIEL Codex Marianus	CC BY-NC-SA 4.0
TOROT (Zographensis, Psalterium Sinaiticum, Suprasliensis, Euchologium, Kiev Missal)	CC BY-NC-SA 4.0
cu-books liturgical texts	MIT
Serbian Mineja, SPC edition (svetosavlje.org)	no explicit licence, research use
Codex Suprasliensis folio images (suprasliensis.obdurodon.org)	CC BY-NC-SA 3.0
Menaion, Monomakh, Fedorovsk, Pomorsky fonts	SIL OFL

The PROIEL and TOROT treebanks are non-commercial, so this model is released under CC BY-NC-SA 4.0.

Limitations

Serbian recension is absent. Medieval Serbian manuscripts such as Miroslavljevo jevanđelje use orthographic conventions the model has not seen. Expect degraded results.
Line crops only. Whole pages are out of distribution.
Parchment and print only. Carved stone, epigraphy and heavily degraded surfaces are outside the training distribution.
Glagolitic is not supported. The Old Church Slavonic sources are Glagolitic manuscripts in Cyrillic transcription; the model has not seen Glagolitic script.
It does not reliably refuse out-of-domain input. Shown Cyrillic that is not Church Slavonic, it will usually attempt a transcription rather than decline.
Serbian register follows the 1868 Daničić translation, not contemporary Serbian.
Non-commercial use only.

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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for jolovicdev/princip-v0.2

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Adapter

(529)

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