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ML Research Hub

Consistency Models Made Easy

🖥 Github: https://github.com/locuslab/ect

📕 Paper: https://arxiv.org/abs/2406.14548v1

🔥Dataset: https://paperswithcode.com/dataset/cifar-10

2.73K viewsedited 04:08

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ExVideo is a tuning technique to improve the ability of models to generate video

ExVideo allows a model to generate 5 times more frames, while requiring only 1.5k GPU training hours on a dataset of 40k videos.

In particular, ExVideo was used to improve the Stable Video Diffusion model to generate long videos up to 128 frames.
The code, article and model are at the links below.

🟡 ExVideo page
🖥 GitHub
🟡 Hugging Face
🟡 Arxiv

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🌟

MG-LLaVA - multimodal LLM with advanced capabilities for working with visual information

Just recently, the guys from Shanghai University rolled out MG-LLaVA - MLLM, which expands the capabilities of processing visual information through the use of additional components: special components that are responsible for working with low and high resolution.

MG-LLaVA integrates an additional high-resolution visual encoder to capture fine details, which are then combined with underlying visual features using the Conv-Gate network.

Trained exclusively on publicly available multimodal data, MG-LLaVA achieves excellent results.

🟡

MG-LLaVA page

🖥

GitHub

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🖥

Unstructured - Python library for raw data preprocessing

- pip install "unstructured[all-docs]"

Unstructured provides components for preprocessing images and text documents; supports many formats: PDF, HTML, Word docs, etc.

Run the library in a container:

docker run -dt --name unstructured downloads.unstructured.io/unstructured-io/unstructured:latest
docker exec -it unstructured bash

🖥

GitHub

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🆒 EAGLE is a method that allows you to generate LLM responses faster

Is it possible to generate LLM response on two RTX 3060s faster than on A100 (which is 16+ times more expensive)?
Yes, it is possible with EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency) and the accuracy of the responses is preserved.

EAGLE allows you to extrapolate the context feature vectors of the second top layer of the LLM, which greatly improves the generation efficiency.

EAGLE is 2x faster than Lookahead (13B), and 1.6x faster than Medusa (13B).
And yes, EAGLE can be combined with other acceleration techniques like vLLM, DeepSpeed, Mamba, FlashAttention, quantization and hardware optimization.

🤗

Hugging Face

💻

GitHub

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2.38K viewsedited 07:36

ML Research Hub

Forwarded from Data Science Premium (Books & Courses)

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1.82K views14:42

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🏠

MedMNIST-C: benchmark dataset based on the MedMNIST+ collection covering 12 2D datasets and 9 imaging modalities.

pip install medmnistc

🖥

Github: https://github.com/francescodisalvo05/medmnistc-api

📕

Paper: https://arxiv.org/abs/2406.17536v2

🔥

Dataset: https://paperswithcode.com/dataset/imagenet-c

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2.44K viewsedited 10:57

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نتطلع للعب معها

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2.17K viewsedited 16:41

ML Research Hub

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2.13K views23:29

ML Research Hub

🔥 ESPNet XEUS is the new speech recognition SoTA.

A multi-lingual speech recognition and translation model from Carnegie Mellon University that is trained in over 4000 languages! 🔥

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> E-Branchformer architecture
> Dataset 8900 hours of audio recordings in over 4023 languages

git lfs install
git clone https://huggingface.co/espnet/XEUS

▪️ HF: https://huggingface.co/espnet/xeus
▪️ Dataset: https://huggingface.co/datasets/espnet/mms_ulab_v2

https://news.1rj.ru/str/DataScienceT

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1.95K viewsedited 06:01

ML Research Hub

🌟

MoMA is an open-source model from ByteDance for generating images from a reference.

MoMA requires no training and can quickly generate image images with high detail accuracy and identity preservation.
The speed of MoMA is achieved by optimizing the attention mechanism, which transfers features from the original image to the diffusion model.
The model is a universal adapter and can be applied to various models without modification.
Today, MoMA outperforms similar existing methods in synthetic tests and allows you to create images with a high level of compliance with the prompt while preserving the style of the reference image as much as possible.

✍️ Recommended parameters for optimizing VRAM consumption:

22 GB or more GPU memory:

args.load_8bit, args.load_4bit = False, False

18 GB or more GPU memory:

args.load_8bit, args.load_4bit = True, False

14 GB or more GPU memory:

args.load_8bit, args.load_4bit = False, True

🟡

MoMA page

🖥

GitHub
🤗 Hugging Face

🟡

Demo

https://news.1rj.ru/str/DataScienceT

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2.16K viewsedited 06:04

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ML Research Hub

⚡️

InternLM introduced XComposer-2.5 - a multi-modal 7B VLM with increased context for input and output.

InternLM-XComposer-2.5 copes with the tasks of text denoscription of images with complex composition, achieving the capabilities of GPT-4V. Trained with alternating 24 KB image-text contexts, it can easily expand to 96 KB contexts via RoPE extrapolation.

Compared to the previous version 2.0, InternLM-XComposer-2.5 has three major improvements:
- understanding of ultra-high resolution;
- detailed understanding of the video;
- process several images in the context of 1 dialogue.

Using extra Lora, XComposer-2.5 is capable of performing complex tasks:
- creation of web pages;
- creation of high-quality text articles with images.

XComposer-2.5 was evaluated on 28 benchmarks, outperforming existing state-of-the-art open source models in 16 benchmarks . It also closely competes with GPT-4V and Gemini Pro on 16 key tasks.

🖥

GitHub

🟡

Arxiv

🟡

Model

🟡

Demo

📺

Demo video

https://news.1rj.ru/str/DataScienceT

✅

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