Large Language Model

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A Coding Implementation to Build an Uncertainty-Aware LLM System with Confidence Estimation, Self-Evaluation, and Automatic Web Research

In this tutorial, we build an uncertainty-aware large language model system that not only generates answers but also estimates the confidence in those answers. We implement a three-stage reasoning pipeline in which the model first produces an answer along with a self-reported confidence score and a justification. We then introduce a self-evaluation step that allows […]

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NVIDIA Releases Nemotron-Cascade 2: An Open 30B MoE with 3B Active Parameters, Delivering Better Reasoning and Strong Agentic Capabilities

NVIDIA has announced the release of Nemotron-Cascade 2, an open-weight 30B Mixture-of-Experts (MoE) model with 3B activated parameters. The model focuses on maximizing ‘intelligence density,’ delivering advanced reasoning capabilities at a fraction of the parameter scale used by frontier models. Nemotron-Cascade 2 is the second open-weight LLM to achieve Gold Medal-level performance in the 2025

NVIDIA Releases Nemotron-Cascade 2: An Open 30B MoE with 3B Active Parameters, Delivering Better Reasoning and Strong Agentic Capabilities Read More »

ServiceNow Research Introduces EnterpriseOps-Gym: A High-Fidelity Benchmark Designed to Evaluate Agentic Planning in Realistic Enterprise Settings

Large language models (LLMs) are transitioning from conversational to autonomous agents capable of executing complex professional workflows. However, their deployment in enterprise environments remains limited by the lack of benchmarks that capture the specific challenges of professional settings: long-horizon planning, persistent state changes, and strict access protocols. To address this, researchers from ServiceNow Research, Mila

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Mistral AI Releases Mistral Small 4: A 119B-Parameter MoE Model that Unifies Instruct, Reasoning, and Multimodal Workloads

Mistral AI has released Mistral Small 4, a new model in the Mistral Small family designed to consolidate several previously separate capabilities into a single deployment target. Mistral team describes Small 4 as its first model to combine the roles associated with Mistral Small for instruction following, Magistral for reasoning, Pixtral for multimodal understanding, and

Mistral AI Releases Mistral Small 4: A 119B-Parameter MoE Model that Unifies Instruct, Reasoning, and Multimodal Workloads Read More »

How to Build Type-Safe, Schema-Constrained, and Function-Driven LLM Pipelines Using Outlines and Pydantic

In this tutorial, we build a workflow using Outlines to generate structured and type-safe outputs from language models. We work with typed constraints like Literal, int, and bool, and design prompt templates using outlines.Template, and enforce strict schema validation with Pydantic models. We also implement robust JSON recovery and a function-calling style that generates validated

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NVIDIA Releases Nemotron 3 Super: A 120B Parameter Open-Source Hybrid Mamba-Attention MoE Model Delivering 5x Higher Throughput for Agentic AI

The gap between proprietary frontier models and highly transparent open-source models is closing faster than ever. NVIDIA has officially pulled the curtain back on Nemotron 3 Super, a staggering 120 billion parameter reasoning model engineered specifically for complex multi-agent applications. Released today, Nemotron 3 Super sits perfectly between the lightweight 30 billion parameter Nemotron 3

NVIDIA Releases Nemotron 3 Super: A 120B Parameter Open-Source Hybrid Mamba-Attention MoE Model Delivering 5x Higher Throughput for Agentic AI Read More »

Microsoft Releases Phi-4-Reasoning-Vision-15B: A Compact Multimodal Model for Math, Science, and GUI Understanding

Microsoft has released Phi-4-reasoning-vision-15B, a 15 billion parameter open-weight multimodal reasoning model designed for image and text tasks that require both perception and selective reasoning. It is a compact model built to balance reasoning quality, compute efficiency, and training-data requirements, with particular strength in scientific and mathematical reasoning and understanding user interfaces. https://arxiv.org/pdf/2603.03975 What the

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Liquid AI Releases LocalCowork Powered By LFM2-24B-A2B to Execute Privacy-First Agent Workflows Locally Via Model Context Protocol (MCP)

Liquid AI has released LFM2-24B-A2B, a model optimized for local, low-latency tool dispatch, alongside LocalCowork, an open-source desktop agent application available in their Liquid4All GitHub Cookbook. The release provides a deployable architecture for running enterprise workflows entirely on-device, eliminating API calls and data egress for privacy-sensitive environments. Architecture and Serving Configuration To achieve low-latency execution

Liquid AI Releases LocalCowork Powered By LFM2-24B-A2B to Execute Privacy-First Agent Workflows Locally Via Model Context Protocol (MCP) Read More »

YuanLab AI Releases Yuan 3.0 Ultra: A Flagship Multimodal MoE Foundation Model, Built for Stronger Intelligence and Unrivaled Efficiency

How can a trillion-parameter Large Language Model achieve state-of-the-art enterprise performance while simultaneously cutting its total parameter count by 33.3% and boosting pre-training efficiency by 49%? Yuan Lab AI releases Yuan3.0 Ultra, an open-source Mixture-of-Experts (MoE) large language model featuring 1T total parameters and 68.8B activated parameters. The model architecture is designed to optimize performance

YuanLab AI Releases Yuan 3.0 Ultra: A Flagship Multimodal MoE Foundation Model, Built for Stronger Intelligence and Unrivaled Efficiency Read More »

How to Build a Stable and Efficient QLoRA Fine-Tuning Pipeline Using Unsloth for Large Language Models

In this tutorial, we demonstrate how to efficiently fine-tune a large language model using Unsloth and QLoRA. We focus on building a stable, end-to-end supervised fine-tuning pipeline that handles common Colab issues such as GPU detection failures, runtime crashes, and library incompatibilities. By carefully controlling the environment, model configuration, and training loop, we show how

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