⏰ Featured AI: Microsoft just dropped Muse & Claude 3.7 Sonnet System Card was just dropped....

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Dive into the hottest AI breakthroughs of the week—handpicked just for you!

Super Important AI News 🔥 🔥 🔥

⭐  Microsoft just dropped Muse - a first of its kind generative AI model trained on Ninja Theory’s multiplayer battle arena game, Bleeding Edge

🧵🧵 Meet Parlant: Reliable Customer-Facing AI Agents with LLMs _(Promoted)

📢 Moonshot AI and UCLA Researchers Release Moonlight: A 3B/16B-Parameter Mixture-of-Expert (MoE) Model Trained with 5.7T Tokens Using Muon Optimizer

🚨 This AI Paper from Weco AI Introduces AIDE: A Tree-Search-Based AI Agent for Automating Machine Learning Engineering

💡💡 Meta AI Introduces MLGym: A New AI Framework and Benchmark for Advancing AI Research Agents

🧲 🧲  Claude 3.7 Sonnet System Card was just dropped!

Featured AI Update 🛡️🛡️🛡️

🔥 Meta AI Introduces MLGym: A New AI Framework and Benchmark for Advancing AI Research Agents

Researchers from the University College London, University of Wisconsin–Madison, University of Oxford, Meta, and other institutes have introduced a new framework and benchmark for evaluating and developing LLM agents in AI research. This system, the first Gym environment for ML tasks, facilitates the study of RL techniques for training AI agents. The benchmark, MLGym-Bench, includes 13 open-ended tasks spanning computer vision, NLP, RL, and game theory, requiring real-world research skills. A six-level framework categorizes AI research agent capabilities, with MLGym-Bench focusing on Level 1: Baseline Improvement, where LLMs optimize models but lack scientific contributions…..

Other AI News 🎖️🎖️🎖️

🚨 Microsoft Researchers Introduces BioEmu-1: A Deep Learning Model that can Generate Thousands of Protein Structures Per Hour on a Single GPU

🧿 Can your language model think strategically? SMART: Boosting LM self-awareness to reduce Tool Overuse & optimize reasoning

 🧩  VLM2-Bench A Closer Look at How Well VLMs Implicitly Link Explicit Matching Visual Cues

📢   Optimizing Training Data Allocation Between Supervised and Preference Finetuning in Large Language Models

🚨 Marktechpost is inviting AI Companies/Startups/Groups to partner for its upcoming AI Magazines on ‘Open Source AI in Production’ and ‘Agentic AI’. _{(Editor’s Message)}

Coding Tutorial 🎖️🎖️🎖️

🖥️ Building a Legal AI Chatbot: A Step-by-Step Guide Using bigscience/T0pp LLM, Open-Source NLP Models, Streamlit, PyTorch, and Hugging Face Transformers (Colab Notebook Included)

In this tutorial, we will build an efficient Legal AI CHatbot using open-source tools. It provides a step-by-step guide to creating a chatbot using bigscience/T0pp LLM, Hugging Face Transformers, and PyTorch. We will walk you through setting up the model, optimizing performance using PyTorch, and ensuring an efficient and accessible AI-powered legal assistant.

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer


model_name = "bigscience/T0pp"  # Open-source and available
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)