Hugging Face develops machine learning models focused on understanding and generating human-like text. Their main products include advanced natural language processing (NLP) models like GPT-2 and XLNet, which can perform tasks such as text completion, translation, and summarization. Users can access these models through a web application and a repository, making it easy for researchers, developers, and businesses to integrate AI into their applications. Unlike many competitors, Hugging Face offers a freemium model, allowing users to access basic features for free while providing subscription plans for advanced functionalities. The company also tailors solutions for large organizations, including custom model training. The goal of Hugging Face is to empower clients to utilize machine learning for various text-related tasks, enhancing their applications with sophisticated language capabilities.

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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. MiniMax is perhaps today best known here in the U.S. as the Singaporean company behind Hailuo, a realistic, high-resolution generative AI video model that competes with Runway, OpenAI’s Sora and Luma AI’s Dream Machine. But the company has far more tricks up its sleeve: today, for instance, it announced the release and open-sourcing of the MiniMax-01 series, a new family of models built to handle ultra-long contexts and enhance AI agent development.The series includes MiniMax-Text-01, a foundation large language model (LLM), and MiniMax-VL-01, a visual multi-modal model. A massive context windowThe LLM, MiniMax-Text-o1, is of particular note for enabling up to 4 million tokens in its context window — equivalent to a small library worth of books

Minimax Unveils Its Own Open Source Llm With Industry-Leading 4M Token Context

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Google’s Gemini AI has quietly upended the artificial intelligence landscape, achieving a milestone few thought possible: the simultaneous processing of multiple visual streams in real time.This breakthrough—allowing Gemini not only to watch live video feeds but also to analyze static images simultaneously—wasn’t unveiled through Google’s flagship platforms. Instead, it emerged from an experimental application called “AnyChat.”This unanticipated leap underscores the untapped potential of Gemini’s architecture, pushing the boundaries of AI’s ability to handle complex, multi-modal interactions. For years, AI platforms have been restricted to managing either live video streams or static photos, but never both at once. With AnyChat, that barrier has been decisively broken.“Even Gemini’s paid service can’t do this yet,” says Ahsen Khaliq, the machine learning lead at Gradio and the creator of AnyChat, in an exclusive interview with VentureBeat

Google’S Gemini Ai Just Shattered The Rules Of Visual Processing—Here’S What That Means For You

Decrypt’s Art, Fashion, and Entertainment Hub. Discover SCENEThe team at Novasky, a ”collaborative initiative led by students and advisors at UC Berkeley’s Sky Computing Lab,” has done what seemed impossible just months ago: They've created a high-performance AI reasoning model for under $450 in training costs.Unlike traditional LLMs that simply predict the next word in a sentence, so-called “reasoning models” are designed to understand a problem, analyze different approaches to solve it, and execute the best solution. That makes these models harder to train and configure, because they must “reason” through the whole problem-solving process instead of just predicting the best response based on their training dataset.That’s why a ChatGPT Pro subscription, which runs the latest o3 reasoning model, costs $200 a month—OpenAI argues that these models are expensive to train and run.The new Novasky model, dubbed Sky-T1, is comparable to OpenAI’s first reasoning model, known as o1—aka Strawberry—which was released in September 2024, and costs users $20 a month. By comparison, Sky-T1 is a 32 billion parameter model capable of running locally on home computers—provided you have a beefy 24GB GPU, like an RTX 4090 or an older 3090 Ti. And it’s free.We're not talking about some watered-down version. Sky-T1-32B-Preview achieves 43.3% accuracy on AIME2024 math problems, edging out OpenAI o1’s 40%

This Free 'Reasoning' Ai Model Beats Openai'S O1—Without A $20 Monthly Fee

Decrypt’s Art, Fashion, and Entertainment Hub. Discover SCENEIllustrious, a text-to-image model based on Stable Diffusion XL, has become so dominant in the AI art community that Civitai, the largest hub for AI art models, had to create a separate category just to handle its massive ecosystem of resources.And it all happened in three months. The secret behind its success? A return to the basics with a twist.While newer models like SD 3.5 and Flux rely on lengthy natural language descriptions, Onoma AI, the developers of Illustrious, took a different approach by leveraging Danbooru tags to help their model understand concepts without having to reinvent the wheel with complex captioning systems.The model's training on Danbooru's vast library of tagged anime images gives it an edge in understanding visual concepts.Each tag in the Danbooru system represents specific elements like character features, clothing items, poses, or backgrounds, allowing for precise control over the generated images without wasting precious tokens on lengthy descriptions.These tags have been around for years and have become kind of a standard for image categorization among art/anime enthusiasts.The model is highly accurate and efficient when it comes to understanding the characteristics of a photo."It's like having an artist who understands exactly what you want without having to explain it in paragraphs," Vishnu, a Discord member who participates in a server focused on NSFW AI content, told Decrypt. "You just need to know the right tags."At its core, Illustrious uses the good old SDXL architecture with a sophisticated dual-encoder system that combines CLIP ViT-L and OpenCLIP ViT-bigG to understand words and associate them with their visual equivalent.The model is capable of processing and generating images at an impressive 1536×1536 resolution, with the capability to stretch up to 2048×2048 and even 3744x3744 without significant quality loss.For context, the original SDXL handled full HD resolutions (1024x1024).Deep diveThe journey to create Illustrious was methodical and deliberate. The initial training phase, which produced version 0.1, processed 7.5M images at 1024×1024 resolution with a batch size of 192 images per batch.The team carefully balanced learning rates, running for 20 epochs (the process in which AI studies 100% of its dataset) to establish a solid foundation. Once the results were satisfactory enough, the team proceeded to increase the size of the dataset and the resolutions used for the next iterations.In the advanced training phase, Illustrious truly began to shine

Lllustrious: The Ai Model That Wants To Rule Anime Art Generation

Choreographed integration of human workers, robotic and agentic systems, and equipment in a facility digital twin. | Source: Accenture, KION Group. NVIDIA Corp.’s CEO Jensen Huang made a slew of announcements yesterday at CES 2025 in Las Vegas. They included the Mega Omniverse blueprint for building industrial robot fleet digital twins, adding generative physical AI to Omniverse, launching the Cosmos World Foundation Model platform, and releasing updates to its Isaac platform. These announcements showed how NVIDIA is doubling down on investing in artificial intelligence technologies, particularly generative AI, for robotics. In addition to its new products, the Santa Clara, Calif.-based company announced that Toyota, Aurora, and Continental are developing their consumer and commercial vehicle fleets with NVIDIA computing and AI

Nvidia Unveils Omniverse Upgrades, Cosmos Foundation Model, And More At Ces

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Researchers at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) have announced the release of LlamaV-o1, a state-of-the-art artificial intelligence model capable of tackling some of the most complex reasoning tasks across text and images.By combining cutting-edge curriculum learning with advanced optimization techniques like Beam Search, LlamaV-o1 sets a new benchmark for step-by-step reasoning in multimodal AI systems.“Reasoning is a fundamental capability for solving complex multi-step problems, particularly in visual contexts where sequential step-wise understanding is essential,” the researchers wrote in their technical report, published today. Fine-tuned for reasoning tasks that require precision and transparency, the AI model outperforms many of its peers on tasks ranging from interpreting financial charts to diagnosing medical images.In tandem with the model, the team also introduced VRC-Bench, a benchmark designed to evaluate AI models on their ability to reason through problems in a step-by-step manner. With over 1,000 diverse samples and more than 4,000 reasoning steps, VRC-Bench is already being hailed as a game-changer in multimodal AI research.LlamaV-o1 outperforms competitors like Claude 3.5 Sonnet and Gemini 1.5 Flash in identifying patterns and reasoning through complex visual tasks, as demonstrated in this example from the VRC-Bench benchmark. The model provides step-by-step explanations, arriving at the correct answer, while other models fail to match the established pattern

Llamav-O1 Is The Ai Model That Explains Its Thought Process—Here’S Why That Matters

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. As enterprises around the world double down on their AI projects, the availability of high-quality training data has become a major bottleneck. While the public web has largely been exhausted as a data source, major players like OpenAI and Google are securing exclusive partnerships to expand their proprietary datasets, further limiting access for others.To address this growing concern, Salesforce has taken a major step in the arena of visual training data. The company has just introduced ProVision, a novel framework that programmatically generates visual instruction data. These datasets are systematically synthesized to enable the training of high-performance multimodal language models (MLMs) that can answer questions about images.The company has already released the ProVision-10M dataset with this approach and is employing it to boost the performance and accuracy of various multimodal AI models.For data professionals, this framework represents a significant advancement

Breaking The Data Bottleneck: Salesforce’S Provision Speeds Multimodal Ai Training With Image Scene Graphs

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Microsoft is doubling down on the potential of small language models (SLMs) with the unveiling of rStar-Math, a new reasoning technique that can be applied to small models to boost their performance on math problems using reasoning techniques — performance similar to, and in some cases exceeding, that of OpenAI’s o1-preview model.While still in a research phase — as outlined in a paper published on pre-review site arXiv.org and credited to eight authors at Microsoft, Peking University and Tsinghua University in China — the technique was applied to several different smaller open-source models including Microsoft’s own Phi-3 mini, Alibaba’s Qwen-1.5B (a 1.5-billion-parameter model), and Qwen-7B (a 7-billion-parameter model). It showed improved performance on all of them, even exceeding OpenAI’s previously most advanced model at the MATH (word problem solving) third-party benchmark of 12,500 questions covering various branches such as geometry and algebra, and all levels of difficulty.Ultimately, according to a post on Hugging Face, the researchers plan to make their code and data available on Github at https://github.com/microsoft/rStar, though one of the paper’s authors, Li Lyna Zhang, wrote in the comments on the Hugging Face post that the team is “still undergoing the internal review process for open-source release.” As such, “the repository remains private for now. Please stay tuned!” Community members expressed enthusiasm, calling the innovations “impressive” and praising the blend of Monte Carlo Tree Search (MCTS) with step-by-step reasoning. One commenter highlighted the simplicity and utility of using Q-values for step scoring, while others speculated on future applications in geometric proofs and symbolic reasoning.This news follows closely on the heels of the open-sourcing of Microsoft’s Phi-4 model, a smaller 14-billion-parameter AI system now available on Hugging Face under the permissive MIT license.While the Phi-4 release has expanded access to high-performance small models, rStar-Math showcases a specialized approach: using smaller AI systems to achieve state-of-the-art results in mathematical reasoning.rStar-Math works by using several different models and components to help a target small model ‘self-evolve’The key to rStar-Math is that it leverages Monte Carlo Tree Search (MCTS), a method that mimics human “deep thinking” by iteratively refining step-by-step solutions to mathematical problems

Microsoft’S New Rstar-Math Technique Upgrades Small Models To Outperform Openai’S O1-Preview At Math Problems

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. A team from Adobe Research and Hong Kong University of Science and Technology (HKUST) has developed an artificial intelligence system that could change how visual effects are made for films, games and interactive media.The technology, called TransPixar, adds a crucial feature to AI-generated videos: the ability to create see-through elements like smoke, reflections, and ethereal effects that blend naturally into scenes. Current AI video tools typically can only generate solid images, making TransPixar a significant technical achievement.“Alpha channels are crucial for visual effects, allowing transparent elements like smoke and reflections to blend seamlessly into scenes,” said Yijun Li, project leader at Adobe Research and one of the paper’s authors. “However, generating RGBA video, which includes alpha channels for transparency, remains a challenge due to limited datasets and the difficulty of adapting existing models.”The breakthrough comes at a critical time as demand for visual effects continues to surge across the entertainment, advertising and gaming industries. Traditional VFX work often requires painstaking manual effort by artists to create convincing transparent effects.A demonstration of TransPixar’s transparency effects shows a photorealistic robot rendered with complex reflective surfaces and seamless alpha-channel blending, allowing the image to be integrated into any background

Smoke, Reflections And Portals: Adobe’S Transpixar Takes Ai Vfx To The Next Level

Smoke, Reflections And Portals: Adobe'S Transpixar Takes Ai Vfx To The Next Level

Hugging Face

Develops advanced AI and NLP models

Hugging Face

Develops advanced AI and NLP models

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