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За прошедший месяц появились 2 новости: · Обновили защиту от входящего спама для услуги «Почта для домена». Фильтрация писем стала лучше за счёт использования единой базы сигнатур, а также внедрения новых алгоритмов обучения. · Объединили все серверы с панелью ISPmanager в единый DNS-кластер. Перенос сайтов между серверами стал удобнее, теперь можно использовать единые серверы ns5.lite-host.in и ns6.lite-host.su при размещении сайтов. Вышли новые обучающие статьи в базе знаний: 1. Как войти в панель администратора WordPress - https://lite.host/faq/hosting/kak-voyti-panel-administratora-wordpress 2. Обработка жалоб и ограничение доступа к сайту - https://lite.host/faq/billing/obrabotka-zhalob-ogranichenie-dostupa-saytu 3. Защита сайта от атак с помощью CloudFlare - https://lite.host/faq/domains/zashchita-sayta-ot-atak-pomoshchyu-cloudflare

Mamba is a new large language model (LLM) architecture that employs a fascinating approach to text processing. What makes it so unique? 1. SSM Instead of RNN: Mamba utilizes state space models (SSM) instead of recurrent neural networks (RNN), which are typically employed in LLMs. SSMs, like RNNs, excel at handling long texts, but SSMs achieve this more efficiently by processing information as a single large matrix operation. 2. Gated MLP for Enhanced Flexibility: Mamba combines SSMs with Gated MLP, a specialized type of neural network that helps the model better "focus" on crucial parts of the text. What are the advantages of this approach? Efficiency: Mamba can reuse computations, saving memory and time. Scalability: Mamba handles very long texts (up to a million words!) better than other models, such as Transformer++. Competitiveness: Mamba demonstrates outstanding results in language understanding tests, even surpassing models twice its size! Overall, Mamba is a promising development in the world of LLMs: It offers a novel approach to text processing that may prove to be more efficient and scalable. Early results appear promising, and it will be intriguing to observe Mamba's performance in the future. What are your thoughts on Mamba? Share your opinion!

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Developers from 01.ai have created an impressive AI model (LLM). Based on the LMSYS Arena results, it approaches the coding quality of top proprietary models. GitHub: 01-ai Hugging Face: 01-ai Official Website: https://01.ai We are eagerly awaiting the release of Yi-Large in open source; it is expected to be excellent! Happy coding, everyone! ☺️

Details from the paper: Stability AI are building bigger and better AI image generators, focusing on a technique called "rectified flow" and a novel architecture called "MM-DiT." Background: Diffusion Models Imagine teaching an AI to paint by first adding noise to a picture until it's unrecognizable, then making it "unpaint" step-by-step back to the original. That's the essence of diffusion models, the current go-to for AI image generation. The AI learns by reversing this noise-adding process, eventually generating new images from pure noise. Problem: The Winding Road of Diffusion The learning path of traditional diffusion models can be indirect and computationally expensive. This is where rectified flow comes in - it creates a straight-line path from noise to data, leading to faster learning and more efficient image generation with fewer steps. Solution: Straightening the Path with Rectified Flow This article delves into the math behind rectified flow, focusing on: Flow trajectories: Defining the precise path the AI takes from noise to image. SNR samplers: Optimizing how the AI learns by focusing on the most important stages of the noise-removal process. The researchers introduce novel SNR samplers like logit-normal sampling and mode sampling with heavy tails, which improve the AI's ability to learn and generate high-quality images. Building a Better Brain: The MM-DiT Architecture To create images from text descriptions, the AI needs to understand both modalities. The MM-DiT architecture tackles this by: Modality-specific representations: Using separate "brains" for text and images, allowing each to excel in its domain. Bi-directional information flow: Enabling the text and image "brains" to communicate and refine the generated image. This results in a more robust and accurate understanding of both text and visual elements, leading to better image quality and prompt adherence. Scaling Up for Stunning Results The researchers trained their MM-DiT models at an unprecedented scale, reaching 8 billion parameters (individual components of the AI's "brain"). This allows the AI to learn more complex patterns and produce highly detailed, realistic images. Key Improvements: Improved autoencoders: Using enhanced image compression techniques allows for better image quality at smaller file sizes. Synthetic captions: Training the AI on both human-written and AI-generated captions leads to a richer understanding of language and concepts. QK-normalization: A technique borrowed from large language models stabilizes the training process for massive AI models. Flexible text encoders: Using multiple text encoders allows for a trade-off between computational cost and accuracy during image generation. Impact and Future Directions: The results are impressive, with the new models outperforming existing state-of-the-art AI image generators in both automated benchmarks and human evaluations. This research paves the way for even more sophisticated and creative AI artists capable of generating stunningly realistic and imaginative content.

Good news: They are planning to release Small (1B), Large (4B), and Huge (8B) Stable Diffusion 3 models.

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Prepare to be amazed! Stability AI has just launched Stable Diffusion 3 Medium, a powerful new AI model that transforms text into stunning images. This latest version boasts major upgrades, delivering better image quality, sharper text within images, and a deeper understanding of your creative prompts. It's also more efficient, using resources wisely. Here's what you need to know: How it works: This model is built on cutting-edge technology called a "Multimodal Diffusion Transformer". Simply put, it learns from massive amounts of data (images and text) and uses this knowledge to create new, unique images based on your text descriptions. For the love of art, not profit: Stable Diffusion 3 Medium is available under a special research license, meaning it's free for non-commercial projects like academic studies or personal art. Want to use it commercially? Stability AI offers a Creator License for professionals and an Enterprise License for businesses. Visit their website or contact them for details. Get creative with ComfyUI: For using the model on your own computer, Stability AI recommends ComfyUI. It's a user-friendly interface to make image generation a breeze. Stable Diffusion 3 Medium is designed for a variety of uses, including: Creating stunning artworks Powering design tools Developing educational and creative applications Furthering research on AI image generation Stability AI emphasizes responsible AI use, taking steps to minimize potential harm. They have implemented safety measures and encourage users to follow their Acceptable Use Policy. This release is a major step forward in AI image generation, offering greater accessibility and impressive capabilities for researchers, artists, and creative minds. We can't wait to see what you create with it! Huggingface repo: https://huggingface.co/stabilityai/stable-diffusion-3-medium Research paper: https://arxiv.org/pdf/2403.03206 NEDNEX article: https://nednex.com/en/stable-diffusion-3-a-leap-forward-in-ai-image-generation/

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