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

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.

Hugging Face has acquired XetHub to accelerate the process of AI democratization. Founded by former Apple engineers, XetHub focuses on simplifying the building of machine learning and AI applications. The team secured $7.5 million in funding from Madrona Ventures in 2021 to accelerate their development. This acquisition signifies Hugging Face, an influential platform in the AI development space, further expanding its technological capabilities and talent pool. The inclusion of XetHub reflects the industry's growth.

Hugging Face and NVIDIA have launched an inference-as-a-service powered by NVIDIA's NIM technology, enhancing prototype development and efficient deployment of open-source AI models. This service notably supports robust models like Llama2 and Mistral AI, offering five times faster processing than traditional methods. Through NVIDIA DGX Cloud, developers can train models, and Hugging Face announced team profitability and expansion to 220 members, ....

Xiaohongshu's official tech account 'Tech Potato' has innovatively opened the world's first AI convenience store, collaborating with quality creators and tech companies to curate AI-related content and produce limited edition products. The store has invited four industry leaders, including the CTO of Hugging Face and the CEO of Guokel, to take on part-time roles, providing traffic support of up to 500,000 Yuan, along with a dedicated exposure plan of 100 million views. Outstanding notes have the chance to win a 10,000 Yuan Xiaomi technology dog. The event encourages participants to publish AI-themed notes using designated topics and tags, with high-quality content receiving official recommendations and tailored weekly features.

Recently, Hugging Face introduced a brand-new AI tool—SmolLM. These are a series of high-performance small language models with parameters ranging from 135M to 1.7B, specifically designed for various devices and applications. Imagine these compact models running efficiently on smartphones and laptops—it's pretty cool!The standout feature of SmolLM models is their small yet powerful nature. They perform exceptionally well with fewer computational resources, helping users protect their privacy.

Hugging Face has updated its Open LLM Leaderboard, a move that will have a significant impact on the landscape of open-source AI development. This improvement comes at a crucial moment in AI development as researchers and companies are facing a plateau in the performance enhancement of large language models (LLMs).

According to Hugging Face CEO Clément Delangue, an increasing number of artificial intelligence startup founders wish to sell their companies. Delangue stated that he receives inquiries from about 10 founders each week who are interested in being acquired.

Apple has made significant advancements in providing cutting-edge on-device AI capabilities for developers. The tech giant recently released 20 new Core ML models and 4 datasets on the leading AI model and code sharing community platform, Hugging Face. This move highlights Apple's commitment to advancing AI while prioritizing user privacy and efficiency.

Researchers have discovered approximately 100 malicious machine learning models on the Hugging Face AI platform that could allow attackers to inject harmful code onto user machines. These malicious AI models exploit methods like PyTorch to execute harmful code, increasing security risks. AI developers should use new tools like Huntr to enhance the security of AI models. The identified malicious models highlight the risks that malicious AI models pose to user environments, necessitating ongoing vigilance and enhanced security.

The JFrog security team has discovered at least 100 malicious AI ML models on the Hugging Face platform. Some of these malicious models are capable of executing code on victim machines, providing persistent backdoors. Security researchers found models with malicious functionalities among the PyTorch and TensorFlow Keras models on the platform. For instance, a model uploaded by a user named baller423 can establish a reverse shell to the host 210.117.212.93. Some malicious models may be motivated by security concerns.

Google and Hugging Face recently announced a strategic partnership aimed at advancing open AI and machine learning. This collaboration will integrate Hugging Face's platform with Google Cloud's infrastructure, including Vertex AI, to make generative AI more accessible for developers. The partnership supports developers in quickly and cost-effectively training, fine-tuning, and serving open models on Google Cloud, utilizing AI to optimize infrastructure such as TPUs and GPUs.

The aMUSEd model launched by Hugging Face can generate images in seconds, faster than competitors like Stable Diffusion. aMUSEd utilizes a lightweight text-to-image model based on Google's MUSE model. It adopts a Masked Image Model (MIM) architecture, reducing inference steps and enhancing generation speed and interpretability. The model is available in a demo on Hugging Face.

The Qwen-72B model from Tongyi Qwen has secured the top position in the Hugging Face open source pre-trained model ranking. The Qwen-72B model boasts 72 billion parameters and an overall score of 73.6. The Hugging Face open source model ranking evaluates top global open source models. Tongyi Qwen's Qwen-72B model excels in reading comprehension, logical reasoning, mathematical calculation, and factual question answering. Tongyi Qwen is launched by Alibaba Cloud.

Meta launches the 'AI Startup Program' at STATION F, aimed at advancing the French entrepreneurial ecosystem with cutting-edge AI models. In collaboration with Hugging Face and Scaleway, the program plans to accelerate the adoption of open-source AI solutions in France. It will support five startups by providing technical guidance, platform access, and computing power to boost their development. Meta, Hugging Face, and Scaleway join forces to strengthen the French AI ecosystem, looking forward to open collaboration.

Hugging Face has established a two-person team named H4, aimed at the open-source development of AI models similar to ChatGPT. The main research focus of the H4 team is on alignment technology, allowing large language models to learn based on human feedback behavior, relying on dedicated clusters for training. The H4 team has released several open-source large language models, including the conversational Zephyr-7B-α and the improved Falcon-40B. H4 does not directly derive commercial benefits from their tools but accelerates Hugging Face.