zsxkib / uform-gen

🖼️ Super fast 1.5B Image Captioning/VQA Multimodal LLM (Image-to-Text) 🖋️

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Input

Output

Run time and cost

This model runs on Nvidia A40 GPU hardware. Predictions typically complete within 86 seconds. The predict time for this model varies significantly based on the inputs.

Readme

UForm

Pocket-Sized Multimodal AI
For Content Understanding and Generation

Welcome to UForm, a multimodal AI library that’s as versatile as it is efficient. UForm tiny embedding models will help you understand and search visual and textual content across various languages. UForm small generative models, on the other hand, don’t only support conversational and chat use-cases, but are also capable of image captioning and Visual Question Answering (VQA). With compact custom pre-trained transformer models, this can run anywhere from your server farm down to your smartphone.

Features

  • Throughput: Thanks to the small size, the inference speed is 2-4x faster than competitors.
  • Tiny Embeddings: 256-dimensional vectors are 2-3x quicker to search than from CLIP-like models.
  • Quantization Aware: Downcasted embeddings from f32 to i8 without losing much recall.
  • Multilingual: Trained on a balanced dataset, the recall is great across over 20 languages.
  • Hardware Friendly: Whether it’s Apple’s CoreML or ONNX, we’ve got you covered.

Models

Embedding Models

Model Parameters Languages Architecture
uform-vl-english 143M 1 2 text layers, ViT-B/16, 2 multimodal layers
uform-vl-multilingual-v2 206M 21 8 text layers, ViT-B/16, 4 multimodal layers
uform-vl-multilingual 206M 12 8 text layers, ViT-B/16, 4 multimodal layers

Generative Models

Model Parameters Purpose Architecture
uform-gen 1.5B Image Captioning, VQA llama-1.3B, ViT-B/16
uform-gen-chat 1.5B Multimodal Chat llama-1.3B, ViT-B/16

Quick Start

Once you pip install uform, fetching the models is as easy as:

import uform

model = uform.get_model('unum-cloud/uform-vl-english') # Just English
model = uform.get_model('unum-cloud/uform-vl-multilingual-v2') # 21 Languages

Producing Embeddings

from PIL import Image
import torch.nn.functional as F

text = 'a small red panda in a zoo'
image = Image.open('red_panda.jpg')

image_data = model.preprocess_image(image)
text_data = model.preprocess_text(text)

image_features, image_embedding = model.encode_image(image_data, return_features=True)
text_features, text_embedding = model.encode_text(text_data, return_features=True)

similarity = F.cosine_similarity(image_embedding, text_embedding)

To search for similar items, the embeddings can be compared using cosine similarity. The resulting value will fall within the range of -1 to 1, where 1 indicates a high likelihood of a match. Once the list of nearest neighbors (best matches) is obtained, the joint multimodal embeddings, created from both text and image features, can be used to better rerank (reorder) the list. The model can calculate a “matching score” that falls within the range of [0, 1], where 1 indicates a high likelihood of a match.

joint_embedding = model.encode_multimodal(
    image_features=image_features,
    text_features=text_features,
    attention_mask=text_data['attention_mask']
)
score = model.get_matching_scores(joint_embedding)

Image Captioning and Question Answering

The generative model can be used to caption images, summarize their content, or answer questions about them. The exact behavior is controlled by prompts.

from uform.gen_model import VLMForCausalLM, VLMProcessor

model = VLMForCausalLM.from_pretrained("unum-cloud/uform-gen")
processor = VLMProcessor.from_pretrained("unum-cloud/uform-gen")

# [cap] Narrate the contents of the image with precision.
# [cap] Summarize the visual content of the image.
# [vqa] What is the main subject of the image?
prompt = "[cap] Summarize the visual content of the image."
image = Image.open("zebra.jpg")

inputs = processor(texts=[prompt], images=[image], return_tensors="pt")
with torch.inference_mode():
     output = model.generate(
        **inputs,
        do_sample=False,
        use_cache=True,
        max_new_tokens=128,
        eos_token_id=32001,
        pad_token_id=processor.tokenizer.pad_token_id
    )

prompt_len = inputs["input_ids"].shape[1]
decoded_text = processor.batch_decode(output[:, prompt_len:])[0]

Multimodal Chat

The generative models can be used for chat-like experiences, where the user can provide both text and images as input. To use that feature, you can start with the following CLI command:

uform-chat --model unum-cloud/uform-gen-chat --image=zebra.jpg
uform-chat --model unum-cloud/uform-gen-chat \
    --image="https://bit.ly/3tIVg9M" \
    --device="cuda:0" \
    --fp16

Multi-GPU

To achieve higher throughput, you can launch UForm on multiple GPUs. For that pick the encoder of the model you want to run in parallel (text_encoder or image_encoder), and wrap it in nn.DataParallel (or nn.DistributedDataParallel).

import uform

model = uform.get_model('unum-cloud/uform-vl-english')
model_image = nn.DataParallel(model.image_encoder)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_image.to(device)

_, res = model_image(images, 0)

Evaluation

Embedding Models

Few retrieval benchmarks exist for multimodal embeddings. The most famous ones for English are “MS-COCO” and “Flickr30k”. Evaluating uform-vl-english model, one can expect the following numbers for search quality.

Dataset Recall @ 1 Recall @ 5 Recall @ 10
Flickr 0.727 0.915 0.949
MS-COCO¹ 0.510 0.761 0.838

For multilingual benchmarks, we’ve created the unum-cloud/coco-sm repository². Evaluating the unum-cloud/uform-vl-multilingual-v2 model, one can expect the following metrics for text-to-image search, compared against xlm-roberta-base-ViT-B-32 OpenCLIP model.

Language OpenCLIP @ 1 UForm @ 1 OpenCLIP @ 5 UForm @ 5 OpenCLIP @ 10 UForm @ 10 Speakers
English 🇺🇸 37.8 37.7 63.5 65.0 73.5 75.9 1‘452 M
Chinese 🇨🇳 27.3 32.2 51.3 59.0 62.1 70.5 1‘118 M
Hindi 🇮🇳 20.7 31.3 42.5 57.9 53.7 69.6 602 M
Spanish 🇪🇸 32.6 35.6 58.0 62.8 68.8 73.7 548 M
Arabic 🇸🇦 22.7 31.7 44.9 57.8 55.8 69.2 274 M
French 🇫🇷 31.3 35.4 56.5 62.6 67.4 73.3 274 M
<summary>All languages.</summary>


Language OpenCLIP @ 1 UForm @ 1 OpenCLIP @ 5 UForm @ 5 OpenCLIP @ 10 UForm @ 10 Speakers
Arabic 🇸🇦 22.7 31.7 44.9 57.8 55.8 69.2 274 M
Armenian 🇦🇲 5.6 22.0 14.3 44.7 20.2 56.0 4 M
Chinese 🇨🇳 27.3 32.2 51.3 59.0 62.1 70.5 1‘118 M
English 🇺🇸 37.8 37.7 63.5 65.0 73.5 75.9 1‘452 M
French 🇫🇷 31.3 35.4 56.5 62.6 67.4 73.3 274 M
German 🇩🇪 31.7 35.1 56.9 62.2 67.4 73.3 134 M
Hebrew 🇮🇱 23.7 26.7 46.3 51.8 57.0 63.5 9 M
Hindi 🇮🇳 20.7 31.3 42.5 57.9 53.7 69.6 602 M
Indonesian 🇮🇩 26.9 30.7 51.4 57.0 62.7 68.6 199 M
Italian 🇮🇹 31.3 34.9 56.7 62.1 67.1 73.1 67 M
Japanese 🇯🇵 27.4 32.6 51.5 59.2 62.6 70.6 125 M
Korean 🇰🇷 24.4 31.5 48.1 57.8 59.2 69.2 81 M
Persian 🇮🇷 24.0 28.8 47.0 54.6 57.8 66.2 77 M
Polish 🇵🇱 29.2 33.6 53.9 60.1 64.7 71.3 41 M
Portuguese 🇵🇹 31.6 32.7 57.1 59.6 67.9 71.0 257 M
Russian 🇷🇺 29.9 33.9 54.8 60.9 65.8 72.0 258 M
Spanish 🇪🇸 32.6 35.6 58.0 62.8 68.8 73.7 548 M
Thai 🇹🇭 21.5 28.7 43.0 54.6 53.7 66.0 61 M
Turkish 🇹🇷 25.5 33.0 49.1 59.6 60.3 70.8 88 M
Ukranian 🇺🇦 26.0 30.6 49.9 56.7 60.9 68.1 41 M
Vietnamese 🇻🇳 25.4 28.3 49.2 53.9 60.3 65.5 85 M
Mean 26.5±6.4 31.8±3.5 49.8±9.8 58.1±4.5 60.4±10.6 69.4±4.3 -
Google Translate 27.4±6.3 31.5±3.5 51.1±9.5 57.8±4.4 61.7±10.3 69.1±4.3 -
Microsoft Translator 27.2±6.4 31.4±3.6 50.8±9.8 57.7±4.7 61.4±10.6 68.9±4.6 -
Meta NLLB 24.9±6.7 32.4±3.5 47.5±10.3 58.9±4.5 58.2±11.2 70.2±4.3 -

Generative Models

For captioning evaluation we measure CLIPScore and RefCLIPScore³.

Model Size Caption Length CLIPScore RefCLIPScore
llava-hf/llava-1.5-7b-hf 7B Long 0.878 0.529
llava-hf/llava-1.5-7b-hf 7B Short 0.886 0.531
Salesforce/instructblip-vicuna-7b 7B Long 0.902 0.534
Salesforce/instructblip-vicuna-7b 7B Short 0.848 0.523
unum-cloud/uform-gen 1.5B Long 0.847 0.523
unum-cloud/uform-gen 1.5B Short 0.842 0.522
unum-cloud/uform-gen-chat 1.5B Long 0.860 0.525
unum-cloud/uform-gen-chat 1.5B Short 0.858 0.525

Results for VQAv2 evaluation.

Model Size Accuracy
llava-hf/llava-1.5-7b-hf 7B 78.5
unum-cloud/uform-gen 1.5B 66.5


¹ Train split was in training data.
² Lacking a broad enough evaluation dataset, we translated the COCO Karpathy test split with multiple public and proprietary translation services, averaging the scores across all sets, and breaking them down in the bottom section.
³ We used apple/DFN5B-CLIP-ViT-H-14-378 CLIP model.

Speed

On RTX 3090, the following performance is expected on text encoding.

Model Multilingual Speed Speedup
bert-base-uncased No 1‘612 sequences/second
distilbert-base-uncased No 3‘174 sequences/second x 1.96
sentence-transformers/all-MiniLM-L12-v2 Yes 3‘604 sequences/second x 2.24
unum-cloud/uform-vl-multilingual-v2 Yes 6‘809 sequences/second x 4.22

On RTX 3090, the following performance is expected on text token generation using float16, equivalent PyTorch settings, and greedy decoding.

Model Size Speed Speedup
llava-hf/llava-1.5-7b-hf 7B ~ 40 tokens/second
Salesforce/instructblip-vicuna-7b 7B ~ 40 tokens/second
unum-cloud/uform-gen 1.5B ~ 140 tokens/second x 3.5

License

All models come under the same license as the code - Apache 2.0.