Ristretto 3B

🚀 Ristretto

Ristretto is a cutting - edge Vision Language Model (VLM) that can deploy dynamic image tokens, adjust token quantities flexibly, and enhance the projector architecture, delivering better performance and versatility.

🚀 Quick Start

Ristretto is a significant advancement in the field of Vision Language Models (VLM). It can deploy dynamic image tokens, allowing for flexible adjustment of token quantities according to task requirements. The enhanced projector architecture supports dynamic token configurations, resulting in improved performance and versatility.

✨ Features

• Dynamic Image Tokens: Capable of deploying and adjusting the quantity of image tokens based on task needs.
• Enhanced Projector Architecture: Supports dynamic token configurations for better performance.

📦 Installation

pip install torch>=2.3.0
pip install transformers==4.37.0

💻 Usage Examples

Basic Usage

import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer
import requests
from io import BytesIO

IMAGENET_MEAN = (0.5, 0.5, 0.5)
IMAGENET_STD = (0.5, 0.5, 0.5)

def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose([
        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
        T.ToTensor(),
        T.Normalize(mean=MEAN, std=STD)
    ])
    return transform

def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float('inf')
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio

def dynamic_preprocess(image, min_num=1, max_num=10, image_size=448, use_thumbnail=False):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set(
        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
        i * j <= max_num and i * j >= min_num)
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size)

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images

def load_image(image_data, input_size=384, max_num=10):
    image = Image.open(image_data).convert('RGB')
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values

model_path = 'LiAutoAD/Ristretto-3B'
model = AutoModel.from_pretrained(
    path,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)


image_url = 'https://github.com/user-attachments/assets/83258e94-5d61-48ef-a87f-80dd9d895524'
response = requests.get(image_url)
image_data = BytesIO(response.content)
pixel_values = load_image(image_data, max_num=10).to(torch.bfloat16).cuda()
generation_config = dict(max_new_tokens=1024, do_sample=True)

# The recommended range for `num_image_token` is 64 to 576, and the value can be adjusted based on task requirements.
num_image_token = 256

# pure-text conversation
question = 'Hello, who are you?'
response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
print(f'User: {question}         Assistant: {response}')

# text-image conversation && multi-round conversation
question = '<image>         Please describe the image.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
print(f'User: {question}         Assistant: {response}')


question = 'What is best title for the image?'
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
print(f'User: {question}         Assistant: {response}')

📚 Documentation

Evaluation

We use VLMEvalKit to evaluate Ristretto-3B. Other results are taken from OpenCompass

📄 License

All of our open - source models are licensed under the Apache - 2.0 license.

Citation