MCP App StoreLe meilleur de l'écosystème IA et MCP, sélectionné chaque jour.
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Research on training LLMs to effectively reason and execute code within Jupyter notebooks. This improves the agent's ability to handle data science and iterative coding workflows.
ModernBERT now supports multiple languages through mmBERT, bringing the efficiency and performance of the ModernBERT architecture to multilingual NLP tasks. This expansion allows for better cross-lingual representation and retrieval.
Google has released EmbeddingGemma, a highly efficient embedding model designed for high-performance retrieval and semantic search. It provides a strong balance of accuracy and speed for RAG pipelines and vector databases.
Hugging Face introduces ahead-of-time (AOT) compilation for ZeroGPU Spaces to improve performance. This optimization reduces startup times and increases execution speed for GPU-accelerated applications.
Integration of Claude with Hugging Face leveraging the Model Context Protocol (MCP) to enable image generation capabilities. This expands Claude's utility by connecting it to specialized HF models via a standardized interface.
A comprehensive technical guide on developing and scaling production-grade CUDA kernels. Essential reading for developers optimizing deep learning performance at the hardware level.
Explores the application of the Model Context Protocol (MCP) to connect AI agents with specialized research tools. Highlights how standardized interfaces can accelerate scientific discovery and data analysis.
Arm and ExecuTorch 0.7 focus on optimizing generative AI for edge devices. This release expands the capability to run efficient AI models on ARM hardware, bridging the gap between cloud and on-device AI.
Arm introduces Neural Super Sampling, utilizing AI to enhance image resolution and quality efficiently. This development highlights the push for on-device AI acceleration for visual tasks.
TextQuests evaluates the performance of LLMs in the complex environment of text-based video games. It examines the models' ability to maintain state and handle emergent gameplay mechanics.
Hugging Face introduces AI Sheets, a tool enabling developers to manipulate and analyze datasets using open AI models directly within a spreadsheet interface.
Technical guide on utilizing Accelerate ND-Parallel for efficient multi-GPU training. Provides essential patterns for scaling AI model training across multiple devices.
Hugging Face integrates Vision Language Model (VLM) alignment into the TRL library, enabling more robust training and fine-tuning of multimodal models. This simplifies the process for developers to align visual and textual representations in open-source AI.
OpenAI has released GPT OSS, a new family of open-source models available on Hugging Face. This marks a significant shift in their release strategy, providing the developer community with high-performance weights for local deployment and research.
A guide on building MCP servers using Python to create an AI-powered shopping assistant integrated with Gradio. Demonstrates the practical implementation of the Model Context Protocol for tool-use in AI apps.
Hugging Face releases the `hf` CLI, a streamlined tool designed to make interacting with the HF Hub faster and more intuitive for developers.
NVIDIA NIM is accelerating the deployment of LLMs on Hugging Face, providing optimized inference containers for a wide range of models. This integration streamlines the path from model selection to production-ready deployment.
Hugging Face updates its Gradio MCP servers with five significant improvements. This enhances the ability to integrate Gradio-based AI apps into MCP-compatible clients.
A new release of state-of-the-art paired encoders and decoders via the Ettin Suite. Useful for developers working on complex embedding and generation tasks.
Hugging Face is transitioning its Hub from Git LFS to Xet to improve performance and scalability for large datasets. This move aims to optimize how massive AI models and datasets are stored and retrieved.