Multimodal Embeddings from 64 to 768 Dimensions • 1B Parameter Chat
Short Texts • Images • 🔜 Video Clips • 🔜 Long Documents
ONNX • CoreML • PyTorch
Python • JavaScript • Swift
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 great for fast 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.
f32 to i8 without losing much recall.For accuracy and speed benchmarks refer to the evaluation page.
Model Parameters Purpose Architectureuform-gen2-dpo 🆕 1.2 B Chat, Image Captioning, VQA qwen1.5-0.5B, ViT-H/14 uform-gen2-qwen-500m 1.2 B Chat, Image Captioning, VQA qwen1.5-0.5B, ViT-H/14 uform-gen ⚠️ 1.5 B Image Captioning, VQA llama-1.3B, ViT-B/16
First, pip install uform. Then, load the model:
from uform import get_model, Modality
processors, models = get_model('unum-cloud/uform3-image-text-english-small')
model_text = models[Modality.TEXT_ENCODER]
model_image = models[Modality.IMAGE_ENCODER]
processor_text = processors[Modality.TEXT_ENCODER]
processor_image = processors[Modality.IMAGE_ENCODER]
Embed images:
import requests from io import BytesIO from PIL import Image image_url = 'https://media-cdn.tripadvisor.com/media/photo-s/1b/28/6b/53/lovely-armenia.jpg' image = Image.open(BytesIO(requests.get(image_url).content)) image_data = processor_image(image) image_features, image_embedding = model_image.encode(image_data, return_features=True)
Embed queries:
text = 'a cityscape bathed in the warm glow of the sun, with varied architecture and a towering, snow-capped mountain rising majestically in the background' text_data = processor_text(text) text_features, text_embedding = model_text.encode(text_data, return_features=True)
For more details check out:
The generative models are natively compatible with
from transformers import AutoModel, AutoProcessor
model = AutoModel.from_pretrained('unum-cloud/uform-gen2-dpo', trust_remote_code=True)
processor = AutoProcessor.from_pretrained('unum-cloud/uform-gen2-dpo', trust_remote_code=True)
prompt = 'Question or Instruction'
image = Image.open('image.jpg')
inputs = processor(text=[prompt], images=[image], return_tensors='pt')
with torch.inference_mode():
output = model.generate(
**inputs,
do_sample=False,
use_cache=True,
max_new_tokens=256,
eos_token_id=151645,
pad_token_id=processor.tokenizer.pad_token_id
)
prompt_len = inputs['input_ids'].shape[1]
decoded_text = processor.batch_decode(output[:, prompt_len:])[0]
For more details check out:
Depending on the application, the embeddings can be down-casted to smaller numeric representations without losing much recall. Switching from f32 to f16 is recommended in almost all cases, unless you are running on very old hardware without half-precision support. Switching to i8 with linear scaling is also possible, but will be noticeable in the recall on larger collections with millions of searchable entries. Similarly, for higher-dimensional embeddings (512 or 768), a common strategy is to quantize them into single-bit representations for faster search.
import numpy as np f32_embedding: np.ndarray = model.encode_text(text_data, return_features=False) f16_embedding: np.ndarray = f32_embedding.astype(np.float16) i8_embedding: np.ndarray = (f32_embedding * 127).astype(np.int8) b1_embedding: np.ndarray = np.packbits((f32_embedding > 0).astype(np.uint8))
Alternative approach to quantization is to use the Matryoshka embeddings, where the embeddings are sliced into smaller parts, and the search is performed in a hierarchical manner.
import numpy as np large_embedding: np.ndarray = model.encode_text(text_data, return_features=False) small_embedding: np.ndarray = large_embedding[:, :256] tiny_embedding: np.ndarray = large_embedding[:, :64]
Both approaches are natively supported by the USearch vector-search engine and the SimSIMD numerics libraries. When dealing with small collections (up to millions of entries) and looking for low-latency cosine distance calculations, you can achieve 5x-2500x performance improvement over Torch, NumPy, SciPy, and vanilla Python using SimSIMD.
from simsimd import cosine, hamming distance: float = cosine(f32_embedding, f32_embedding) # 32x SciPy performance on Apple M2 CPU distance: float = cosine(f16_embedding, f16_embedding) # 79x SciPy performance on Apple M2 CPU distance: float = cosine(i8_embedding, i8_embedding) # 133x SciPy performance on Apple M2 CPU distance: float = hamming(b1_embedding, b1_embedding) # 17x SciPy performance on Apple M2 CPU
Similarly, when dealing with large collections (up to billions of entries per server) and looking for high-throughput search, you can achieve 100x performance improvement over FAISS and other vector-search solutions using USearch. Here are a couple of examples:
from usearch.index import Index f32_index = Index(ndim=64, metric='cos', dtype='f32') # for Matryoshka embeddings f16_index = Index(ndim=64, metric='cos', dtype='f16') # for Matryoshka embeddings i8_index = Index(ndim=256, metric='cos', dtype='i8') # for quantized embeddings b1_index = Index(ndim=768, metric='hamming', dtype='b1') # for binary embeddings
PyTorch is a heavy dependency to carry, especially if you run on Edge or IoT devices. Using vanilla ONNX runtime, one can significantly reduce memory consumption and deployment latency.
$ conda create -n uform_torch python=3.10 -y
$ conda create -n uform_onnx python=3.10 -y
$ conda activate uform_torch && pip install -e ".[torch]" && conda deactivate
$ conda activate uform_onnx && pip install -e ".[onnx]" && conda deactivate
$ du -sh $(conda info --envs | grep 'uform_torch' | awk '{print $2}')
> 5.2G ~/conda/envs/uform_torch
$ du -sh $(conda info --envs | grep 'uform_onnx' | awk '{print $2}')
> 461M ~/conda/envs/uform_onnx
Most of that weight can be further reduced down to 100 MB for both the model and the runtime. You can pick one of many supported ONNX execution providers, which includes XNNPACK, CUDA and TensorRT for Nvidia GPUs, OpenVINO on Intel, DirectML on Windows, ROCm on AMD, CoreML on Apple devices, and more to come.
The generative models can be used for chat-like experiences in the command line. For that, you can use the uform-chat CLI tool, which is available in the UForm package.
$ pip install uform $ uform-chat --model unum-cloud/uform-gen2-dpo --image=zebra.jpg $ uform-chat --model unum-cloud/uform-gen2-dpo \ > --image="https://bit.ly/3tIVg9M" \ > --device="cuda:0" \ > --fp16
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