Le meilleur du dev et de l'IA, scoré chaque jour par un agent. Filtré, résumé, classé. Aucune couleur, aucun bruit — juste la matière.
The post explains how to run OlympicCoder-7B locally using LM Studio and a VS Code extension. It walks through installing LM Studio, loading OlympicCoder-7B (4-bit GGUF), and wiring LM Studio to VS Code with Continue.dev, enabling code completion, generation, explanation, and tests. OlympicCoder is open-source and optimized on the CodeForces-CoTs dataset, not a Claude-like assistant.
Hugging Face submitted its response to the White House AI Action Plan, arguing that open models and open science are foundational to AI success. They propose three recommendations: recognize open source as fundamental, prioritize efficiency and reliability to enable broad adoption, and secure AI through open, traceable, and transparent systems. They illustrate with examples like OlympicCoder beating Claude 3.7 with 7B parameters and open OLMo 2 models.
At GTC 2025, NVIDIA unveiled three open-source releases for physical AI: Cosmos Transfer (a 7B World Foundation Model with multicontrols for controllable world scenes), the Open Physical AI Dataset, and Isaac GR00T N1, the first open humanoid-reasoning foundation model. Cosmos Transfer uses per-sensor ControlNets and adaptive spatiotemporal maps to generate photorealistic sequences with controllable layout and motion.
Xet migration moved roughly 6% of Hugging Face Hub download traffic to Xet storage, validating the architecture under real-world usage. Unlike LFS that deduplicates at the file level, Xet uses content-defined chunking (~64 KB) to deduplicate at the byte level, so edits affect only changed chunks and large files transfer faster. The system combines a Xet-aware client, a content-addressed store with an LFS bridge, and S3 for final storage, all geared toward production readiness.
Gemma 3 is Google's open, multimodal LLM family released in 1B, 4B, 12B, and 27B variants with up to 128k context and support for 140+ languages. The 4B/12B/27B models are multimodal (images+text) using SigLIP, with pan-and-scan inference and memory-optimized KV cache; the 1B remains text-only. It can also be used as a text-only LLM by omitting the vision encoder.
Open R1: Update #3 reports progress on reproducing the code-reasoning aspects of DeepSeek-R1, including a new CodeForces-CoTs dataset with ~100k samples in C++ and Python. It introduces the IOI benchmark and OlympicCoder models (7B and 32B), showing that training on CodeForces-CoTs yields top-tier performance, with OlympicCoder-32B outperforming several open-weight models. The post also covers dataset construction, benchmarks, and practical tips for scaling and prompting, such as prefilling with
L2D is introduced as the world's largest multimodal self-driving dataset (90+ TB, 5000+ hours, 30 cities). It provides expert and student driving policies with natural language instructions and future waypoints, enabling end-to-end training of automotive AI and inviting community contributions.
This tutorial shows how to run LLMs on-device via a React Native app, downloading GGUF models from Hugging Face and loading them with llama.rn (llama.cpp). It covers model sizing and quantization formats (Q4_0, Q3_K, IQ2_XXS, etc.), and outlines a complete app workflow from environment setup to debugging, all aimed at offline, privacy-preserving mobile AI.
Cohere's Aya Vision unveils 8B and 32B multilingual vision-language models for 23 languages, with strong results on AyaVisionBench and mWildVision. It uses synthetic annotations, multilingual data expansion, and model merging, and handles high-res images via dynamic tiling and Pixel Shuffle downsampling before the vision-language connector and LLM. Training is in two stages: vision-language alignment with frozen encoders, then supervised fine-tuning across multilingual tasks.
Hugging Face and JFrog announce a security partnership to integrate JFrog's scanner into the Hugging Face Hub, reducing false positives by analyzing code inside model weights rather than relying on pattern matching. The collaboration targets risks from serialization formats like pickle that can enable code execution, and all public repos will be scanned automatically as you push.
Cet article présente Arize Phoenix pour tracer, évaluer et déboguer les agents IA en temps réel. Il démontre comment activer le tracing via OpenTelemetry et OpenInference, et propose des étapes concrètes pour installer smolagents, configurer un agent et lancer des tests. Des extraits de code et d’utilisation servent de guide pratique pour améliorer la traçabilité et la performance.
Hugging Face and IISc/ARTPARK partner to enable access to Vaani, India's diverse open-source, multi-modal dataset. Vaani targets 54 languages, 150k hours of speech and 15k hours of transcribed data from about 1 million people across 773 districts, with Phase 1 already open-sourced and Phase 2 expanding reach. This dataset supports ASR, TTS, language identification, and speaker verification, empowering robust Indic-language models and code-switching applications.
FastRTC is a Python library that simplifies building real-time audio and video AI apps. It offers automatic voice detection and turn-taking, a built-in Gradio UI, WebRTC/WebSocket support, and easy deployment to FastAPI, plus utilities for STT/TTS and phone-based access. The post walks through a hello-world echo example and a level-up LLM chat flow with SambaNova/OpenAI integration.
Cet article présente le déport du décodeur VAE vers une endpoint distante pour réduire la mémoire et la latence lors de l’inférence de modèles de diffusion en latent. Il détaille l’intégration avec diffusers et remote_decode et montre des cas d’usage (Stable Diffusion v1.5, Flux, HunyuanVideo) via des exemples de code et les avantages (aucun stockage des données, open source).
SigLIP 2 expands Google's multilingual vision-language encoder family by adding additional training objectives to SigLIP's sigmoid loss, boosting semantic understanding, localization, and dense features. It outperforms SigLIP across scales on zero-shot classification, image-text retrieval, and transfer tasks, and introduces a dynamic resolution (naflex) variant for aspect-ratio-sensitive downstream work. The release catalogs multiple models (Base, Large, So400m, Giant) with varied patch sizes, 2
SmolVLM2 rend la compréhension vidéo accessible sur tous les devices via trois tailles (2.2B, 500M et 256M) et des APIs MLX dès le départ. Par rapport à la génération précédente, il optimise la mémoire et excelle sur Video-MME, avec des démos et une interface interactive pour tester la vision et la compréhension vidéo même dans un Colab gratuit.
Google publie PaliGemma 2 Mix, une famille de modèles vision-langage fine-tunés sur OCR, captioning et autres tâches. Disponibles en 3B/10B/28B et résolutions jusqu'à 896x896, ils permettent d’estimer les performances après fine-tuning sur des tâches en aval. L'article détaille des prompts open-ended et des prefixes (caption, describe, ocr, answer) ainsi que des invites pour détection et segmentation, avec une démo.
Hugging Face adds Hyperbolic, Nebius AI Studio, and Novita as serverless inference providers, integrated in the Hub and accessible via JS/Python SDKs. It outlines two usage modes (custom provider keys or routing through HF) with code examples for DeepSeek-R1 and Flux.1, and explains billing depending on the routing mode.
Fireworks.ai est désormais un fournisseur d’inférence pris en charge sur Hugging Face Hub. L’article présente comment lancer de l’inférence serverless via Fireworks.ai sur Python, JS et HTTP, avec des exemples sur des modèles comme DeepSeek-R1 et Llama-3, et détaille la facturation et les crédits PRO.
Math-Verify a remis à plat l'évaluation des LLM sur le Open LLM Leaderboard, en réévaluant 3 751 modèles sur 1 324 problèmes de maths difficiles. L'article explique les failles de l'ancienne méthode (format de réponse, parsing SymPy) et décrit les améliorations qui permettent une comparaison plus juste et robuste des modèles.