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Develop a Multi-Tool AI Agent with Secure Python Execution using Riza and Gemini

In this tutorial, we’ll harness Riza’s secure Python execution as the cornerstone of a powerful, tool-augmented AI agent in Google Colab. Beginning with seamless API key management, through Colab secrets, environment variables, or hidden prompts, we’ll configure your Riza credentials…

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Mistral AI Releases Magistral Series: Advanced Chain-of-Thought LLMs for Enterprise and Open-Source Applications

Mistral AI has officially introduced Magistral, its latest series of reasoning-optimized large language models (LLMs). This marks a significant step forward in the evolution of LLM capabilities. The Magistral series includes Magistral Small, a 24B-parameter open-source model under the permissive…

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NVIDIA Researchers Introduce Dynamic Memory Sparsification (DMS) for 8× KV Cache Compression in Transformer LLMs

As the demand for reasoning-heavy tasks grows, large language models (LLMs) are increasingly expected to generate longer sequences or parallel chains of reasoning. However, inference-time performance is severely limited by the memory footprint of the key–value (KV) cache, not just…

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How Much Do Language Models Really Memorize? Meta’s New Framework Defines Model Capacity at the Bit Level

Introduction: The Challenge of Memorization in Language Models Modern language models face increasing scrutiny regarding their memorization behavior. With models such as an 8-billion parameter transformer trained on 15 trillion tokens, researchers question whether these models memorize their training data…

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ether0: A 24B LLM Trained with Reinforcement Learning RL for Advanced Chemical Reasoning Tasks

LLMs primarily enhance accuracy through scaling pre-training data and computing resources. However, the attention has shifted towards alternate scaling due to finite data availability. This includes test-time training and inference compute scaling. Reasoning models enhance performance by emitting thought processes…

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