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NVIDIA NIM offre un conteneur unique pour deployer rapidement une large gamme de LLM via Hugging Face, en automatisant l’adaptation, l’analyse du modele et le choix du backend (TensorRT-LLM, vLLM, SGLang). Il prend en charge Hugging Face, GGUF et TensorRT-LLM et illustre le deployment avec Codestral-22B via une commande Docker et tokens API.
Arc Institute lance le Virtual Cell Challenge, qui vise à entraîner un modèle capable de prédire l’effet du silençage d’un gène sur une cellule, même dans des types cellulaires inédits (contexte généralisation). Le jeu de données réunit environ 300k profils RNA‑seq d’une cellule unique et propose un cadre mathématique pour séparer le signal du perturbation, l’hétérogénéité et le bruit technique, avec des architectures comme le State Transition Model et le State Embedding Model.
AI evaluation should shift from recalling facts to forecasting future events. FutureBench uses real-world prediction markets and live news to test agents' ability to reason under uncertainty and synthesize information to predict outcomes.
Consilium est une plateforme qui fait débattre et faire consensus entre plusieurs LLMs via des discussions structurées, avec des modes comme consensus, vote majoritaire ou classement par choix. Déployée comme interface Gradio et serveur MCP, elle visualise une table ronde et les échanges des experts. L'article relie l'idée au MAI-DxO de Microsoft pour démontrer l'efficacité du multi-LLM.
Gradio has released version 5.38.0 to enhance MCP servers with Seamless Local File Support via a new File Upload endpoint, real-time progress streaming for MCP clients, and a one-line OpenAPI-to-MCP conversion using gr.load_openapi. The update also improves authentication by allowing server arguments to be declared as gr.Header and surfaced in docs.
Ettin introduces the first SoTA paired encoder-only and decoder-only models (17M–1B params) trained identically for apples-to-apples comparisons. It extends the ModernBERT recipe to both architectures, with encoders beating ModernBERT and decoders beating Llama 3.2 and SmolLM2, while preserving architecture-specific advantages.
Hugging Face has deployed Xet as the Hub’s storage backend, migrating hundreds of petabytes and millions of repos with minimal disruption. The migration relies on a Git LFS Bridge and background content migrations to support both LFS and Xet, allowing a no-hard-cutover transition. The approach aims to scale storage with AI workloads while preserving existing workflows.
Kimina-Prover-72B uses a Test-Time Reinforcement Learning (TTRL) search to autonomously discover and reuse lemmas for long-horizon Lean proofs, plus an error-fixing module that interprets Lean messages for targeted corrections. It achieves a state-of-the-art miniF2F performance (92.2% pass rate) and shows pass@1/32/1024 of 63.9/84.0/87.7, with two distilled variants released (8B and 1.7B).
ScreenEnv is a Python library that runs isolated Ubuntu desktop environments in Docker to test and deploy GUI agents with full desktop control, including window management and file operations. It supports both the Model Context Protocol (MCP) and a Direct Sandbox API, enabling flexible integration with existing backends or AI systems. The article provides setup examples and a quick path to build a custom Desktop Agent using smolagents.
Building the Hugging Face MCP Server enables customized AI Assistants to access the Hub and thousands of apps through a single URL. The article compares MCP transports (Streamable HTTP vs SSE) and outlines three patterns: Direct Response, Request Scoped Streams, and Server Push Streams, with their trade-offs and needed connection management. It also covers making the server dynamic and remotely configurable, plus client-side usage hints (TypeScript/Python).
Asynchronous inference decouples action prediction from execution in robotic policies, reducing runtime lag and enabling replanning with action chunks. The article describes a two-component architecture (PolicyServer and RobotClient) using gRPC to achieve ~2× speedups and continuous operation, and explains why sequential inference falls short.
The article introduces the Model Context Protocol (MCP) and explains how MCP servers can extend LLMs with new abilities via an app-store-like ecosystem. It guides readers to find MCP-compatible spaces in Hugging Face Spaces and demonstrates wiring a Flux.1 Kontext[dev] MCP server to an LLM to edit images from text prompts.
Reachy Mini is an open-source, programmable robot for human-robot interaction and AI prototyping. It runs Python (and soon JS/Scratch), ships as a kit, and starts at $299, with a wireless compute version at $449. It emphasizes open hardware/software, a growing community, and privacy controls by design.
Hugging Face and AMD optimize custom kernels for MI300X to boost Llama 3.1 405B inference in FP8 on 8 GPUs. They combine fused residual/RMS norm/FP8 conversion, SwiGLU, and a Skinny GEMM kernel, with benchmarks and open-source tooling in hf-rocm-kernels.
Hugging Face outlines three major alerts that bolster production infrastructure monitoring. The highlighted NAT Gateway throughput alert acts as an early warning for unusual egress traffic and helps with cost optimization, while other alerts cover Kubernetes API request errors, rate limiting, and a bonus alert for new clusters sending zero metrics.
SmolLM3 is a competitive open 3B model (11T tokens) with 128k long context via NoPE and YaRN, and multilingual support across six languages. It uses a Llama-based decoder with Grouped Query Attention, intra-document masking, and training-stability tweaks, plus a dual Instruct/Reasoning model.
Efficient MultiModal Data Pipeline identifies data pipeline waste in multimodal training, like idle GPUs and excessive padding. It outlines a five-stage approach—from visualizing the dataset to packing batches with a knapsack strategy—to reduce padding and maximize throughput. The post also introduces an iterable dataset and a repository for practical reproduction.
Annonce de la compétition NeurIPS 2025 E2LM visant à capturer les signaux en phase précoce d'entraînement des LLMs dans le domaine scientifique. Les submissions seront évaluées via ScoreSQ, ScoreRC et ScoreCS, avec Score = α1·ScoreSQ + α2·ScoreRC + α3·ScoreCS et des poids α1=0.5, α2=0.1, α3=0.4. L'inscription se fait sur Hugging Face et l'évaluation s'appuie sur lm-evaluation-harness, avec des phases allant de juillet à novembre 2025 et résultats prévus le 04 novembre 2025.
Sentence Transformers can finetune sparse encoder/embedding models for retrieval, hybrid search, and reranking. The post outlines training components (model, datasets, losses, trainer, evaluators) and provides practical examples, including using naver/splade-v3 and decoding sparse embeddings for interpretability.
Le NVIDIA Llama Nemotron Nano VL est un modèle Vision-Language 8B optimisé pour l’identification et l’OCR de documents, offrant une haute précision OCRBench v2 et une extraction fiable de texte, tableaux et éléments visuels. Disponible sur Hugging Face, il s’appuie sur Llama-3.1-8B-Instruct et C-RADIOv2-VLM-H et peut être post-entraîné via NVIDIA NeMo; un tutoriel Notebook permet de construire rapidement des solutions d’automatisation pour factures, contrats et documents de santé.