Beijing TuSimple Technology Co., Ltd. officially launched its first 'Image-to-Video' large model - 'Ruyi' on December 17, 2024, and has made the Ruyi-Mini-7B version open source for users to download from the huggingface platform. TuSimple was founded in 2015 and is headquartered in San Diego, California, focusing on the application of AI technology across various industries, including animation gaming and transportation.

Recently, the social media platform Bluesky faced a significant data scraping incident. A machine learning librarian, Daniel van Strien, scraped over one million public user posts from Bluesky's API and uploaded this data to the AI company Hugging Face. The dataset includes users' Decentralized Identifiers (DIDs) and a range of features to search for specific user content.

Teuken-7B, a language model with 7 billion parameters, has now been launched on Hugging Face, supporting all 24 official EU languages. This model was developed by the EU OpenGPT-X research project and is available as an open-source project for users. Unlike most AI language models that are English-centric, Teuken-7B was built from the ground up, with approximately half of its training data sourced from non-English European languages. Image source note: The image is AI-generated.

Microsoft's recently launched screen content parsing tool OmniParser has jumped to the top of the Most Popular Models on the artificial intelligence open-source platform HuggingFace this week. According to HuggingFace co-founder and CEO Clem Delangue, this is the first parsing tool to achieve this honor in the field. OmniParser is primarily used to convert screenshots into structured data, aiding other systems in better understanding and processing graphical user interfaces. The tool utilizes a collaborative approach with multiple models: YO

Meta recently announced that its MobileLLM model is now open to researchers, allowing users to download and use these models for free on the Hugging Face platform, fully integrated with the Transformers library. This initiative aims to promote research and development of large language models (LLMs) on mobile devices, providing a broader range of tools and resources for developers and academia. MobileLLM represents a significant advancement for Meta in the field of mobile AI.

At the recent Gemma Developer Day held in Tokyo, Google officially launched its new Japanese version of the Gemma AI model. This model's performance rivals that of GPT-3.5, but it has a parameter count of only 2 billion, making it very compact and suitable for operation on mobile devices. The newly released Gemma model excels in Japanese processing while also maintaining its capabilities in English. This is particularly important for small models, as they may face 'catastrophic forgetting' when fine-tuning for new languages.

Recently, ByteDance officially addressed rumors regarding an 'intern disrupting large model training'. The company confirmed that an intern did maliciously interfere with the model training tasks of the commercialization technology team's research project. However, this action did not affect the company's official commercial projects and online business, nor did it involve other large model businesses of ByteDance. Moreover, ByteDance pointed out that claims circulating online about 'involving over 8,000 nodes and causing losses of over ten million dollars' are seriously exaggerated. According to public reports, the intern was part of ByteDance's commercialization technology team.

Recently, Hugging Face released a new Python package called 'OpenAI-Gradio', aimed at helping developers quickly create AI-driven web applications. The launch of this tool signifies a major shift in AI integration, streamlining the process of using OpenAI's large language models (LLMs) and allowing developers of various skill levels to easily get started. With OpenAI-Gradio, developers can launch their AI applications in just a few minutes.

Recently, the artificial intelligence platform Hugging Face announced that the number of publicly available AI models has exceeded one million. This milestone not only signifies Hugging Face's rapid development in the AI field but also reflects the increasing importance of specialized AI solutions. Image source note: The image was generated by AI, image authorized by service provider Midjourney. Hugging Face co-founder Clément Delang

Recently, OpenAI launched a groundbreaking multilingual dataset designed to evaluate AI performance across 14 languages, including Arabic, German, Swahili, Bengali, and Yoruba. This dataset, named 'Multilingual Massive Multitask Language Understanding' (MMMLU), has been published on the open data platform Hugging Face, marking another significant advancement for OpenAI in the global AI landscape. Dataset link: https://huggingface

Hugging Face recently released LightEval, a lightweight AI evaluation tool designed to assist enterprises and research institutions in more accurately assessing large language models. This tool emphasizes the importance of evaluation in the application of AI technologies, helping to ensure the accuracy, impartiality, and alignment of model outputs with business objectives. Addressing the limitations of traditional evaluation methods, LightEval offers customized evaluation solutions and integrates with other tools from Hugging Face, such as Datat.

Getty Images has announced a significant initiative by providing AI developers with a training dataset that includes 3750 high-quality photos across 15 categories, aimed at supporting the development of AI applications. These photos are now available on the Hugging Face platform, and users can access them for free after agreeing to the terms of service and providing their contact information. This move is intended to attract businesses and developers while promoting their paid licensing services. Getty Images claims to have over 572 million photos, with more than 200 million available for commercial use.

In the field of game development, large models are becoming an indispensable 'think tank', showcasing comprehensive capabilities from AI character generation to scene construction. However, improvements are still needed in game scene understanding, image recognition, and content description. A research team from Alberta, Canada has launched the open-source large model VideoGameBunny (VGB), optimized for game development, featuring multi-language support, high customizability, powerful text generation capabilities, easy accessibility, compatibility with various development environments, a wealth of model files, and active community support. VGB supports multiple...

Runway secretly removed the codebase of Stable Diffusion v1.5 from the Hugging Face platform, sparking significant concern in the tech community. This action was taken without notifying Hugging Face or community members, leaving developers and researchers dependent on these resources in a difficult position. Stable Diffusion v1.5 was a key part of a research project involving Runway, but was ultimately taken down due to copyright issues. Runway's....

The AI company Runway's content on the HuggingFace platform was cleared, sparking widespread attention both within and outside the industry. As an open-source platform for machine learning models and datasets, the content on HuggingFace is crucial for researchers and developers. This incident has ignited heated discussions on Twitter, highlighting the challenges platforms face in responding to emergencies and protecting user rights. As a key player in the AI community, the disappearance of Runway's content affects numerous researchers, raising concerns about data security, corporate responsibility, and user trust.

Meta has released a mid-term update showing a significant increase in the adoption rate of its Llama series models, especially after the launch of Llama 3.1. The downloads of the Llama models on the Hugging Face platform are approaching 350 million, a tenfold increase from last year, attracting well-known companies such as Zoom, Spotify, AT&T, and Goldman Sachs. The development of open-source AI not only approaches the performance of closed models but also achieves widespread application at the enterprise level.

LeRobot is an open-source project released by HuggingFace that provides a comprehensive guide aimed at simplifying the entry barriers for AI-driven robotics technology, supporting the open-source ecosystem in the robotics field. The guide is created by LeRobot co-founder Remi Cadene and targets a broad audience, aiming to popularize the application of AI in robotics and encourage innovation. Based on the open-source robotics kit Koch v1.1, the guide covers the entire process of assembling, configuring, and training AI-controlled robots, focusing on two 6-degree-of-freedom robotic arms.