---
library_name: transformers
pipeline_tag: image-text-to-text
datasets: Vikhrmodels/LLaVA-Instruct-ru
language: 
- ru
license: apache-2.0
tags:
- multimodal
- vision
- image-text-to-text
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
Русскоязычная версия Idefics, обученная на русифицированном сабсете LLaVA.

Первая версия: слабо развит возможность вести диалог и не работает нарезка изображения (LLaVA-Next like подход). SFT был без текстовых данных, так что вполне возможно просадка по качетсву на text-only данных.

Обучение было в int4 с QLoRA на consumer-grade железе. В след итерациях планируется добавить больше данных и обучить на большем железе. 

Скрипты для обучения/инференса добавлю позже.


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Model type:** ruIdefics2
- **Language(s) (NLP):** Russian
- **License:** Apache-2.0
- **Finetuned from model [optional]:** Idefics2

# How to Get Started

This section shows snippets of code for generation for `idefics2-8b-base` and `idefics2-8b`. The codes only differ by the input formatting. Let's first define some common imports and inputs.

```python
import requests
import torch
from PIL import Image
from io import BytesIO

from transformers import AutoProcessor, AutoModelForVision2Seq
from transformers.image_utils import load_image

DEVICE = "cuda:0"

image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg")
image2 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg")
image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg")

processor = AutoProcessor.from_pretrained("GeorgeBredis/ruIdefics2-ruLLaVA-merged")
model = AutoModelForVision2Seq.from_pretrained(
    "GeorgeBredis/ruIdefics2-ruLLaVA-merged",
).to(DEVICE)

# Create inputs
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "Что изображено на данной картинке?"},
        ]
    }
]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=prompt, images=[image1, image2], return_tensors="pt")
inputs = {k: v.to(DEVICE) for k, v in inputs.items()}


generated_ids = model.generate(**inputs, max_new_tokens=500)
generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)

print(generated_texts)
```