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  • ⏰ Featured AI: 4 Open-Source Alternatives to OpenAI’s $200/Month Deep Research.....

⏰ Featured AI: 4 Open-Source Alternatives to OpenAI’s $200/Month Deep Research.....

Hi There,

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

Super Important AI News 🔥 🔥 🔥

 ByteDance Proposes OmniHuman-1: An End-to-End Multimodality Framework Generating Human Videos based on a Single Human Image and Motion Signals

🧵🧵 Meet IntellAgent: An Open-Source Multi-Agent Framework to Evaluate Complex Conversational AI System (Promoted)

📢 Deep Agent Released R1-V: Reinforcing Super Generalization in Vision-Language Models with Cost-Effective Reinforcement Learning to Outperform Larger Models

🚨 Meta AI Introduces VideoJAM: A Novel AI Framework that Enhances Motion Coherence in AI-Generated Videos

💡💡 Gemini 2.0 is now available to everyone via the Gemini API in Google AI Studio and Vertex AI.

🧲 🧲  4 Open-Source Alternatives to OpenAI’s $200/Month Deep Research AI Agent

Featured AI Update 🛡️🛡️🛡️

🔥 s1: A Simple Yet Powerful Test-Time Scaling Approach for LLMs

Researchers from Stanford University, the University of Washington, the Allen Institute for AI, and Contextual AI have proposed a streamlined approach to achieve test-time scaling and enhanced reasoning capabilities. Their method centers on two key innovations: the carefully curated s1K dataset comprising 1,000 questions with reasoning traces, selected based on difficulty, diversity, and quality criteria, and a novel technique called budget forcing. This budget-forcing mechanism controls test-time computation by either cutting short or extending the model’s thinking process through strategic “Wait” insertions, enabling the model to review and correct its reasoning. The approach was implemented by fine-tuning the Qwen2.5-32B-Instruct language model on the s1K dataset.

The s1-32B model demonstrates significant performance improvements through test-time compute scaling with budget forcing. s1-32B operates in a superior scaling paradigm compared to the base Qwen2.5-32B-Instruct model using majority voting, validating the effectiveness of sequential scaling over parallel approaches. Moreover, s1-32B emerges as the most efficient open data reasoning model in sample efficiency, showing marked improvement over the base model with just 1,000 additional training samples. While r1-32B achieves better performance it requires 800 times more training data. Notably, s1-32B approaches Gemini 2.0 Thinking’s performance on AIME24, suggesting successful knowledge distillation.....

Other AI News 🎖️🎖️🎖️

🚨 Meet IntellAgent: An Open-Source Multi-Agent Framework to Evaluate Complex Conversational AI System (Promoted)

🧿 Google DeepMind Achieves State-of-the-Art Data-Efficient Reinforcement Learning RL with Improved Transformer World Models

 🧩  s1: A Simple Yet Powerful Test-Time Scaling Approach for LLMs

📢   Meet Satori: A New AI Framework for Advancing LLM Reasoning through Deep Thinking without a Strong Teacher Model

Coding Tutorial 🎖️🎖️🎖️

In this tutorial, we’ll walk through how to set up and perform fine-tuning on the Llama 3.2 3B Instruct model using a specially curated Python code dataset. By the end of this guide, you’ll have a better understanding of how to customize large language models for code-related tasks and practical insight into the tools and configurations needed to leverage Unsloth for fine-tuning.

Installing Required Dependencies

!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install "git+https://github.com/huggingface/transformers.git"
!pip install -U trl
!pip install --no-deps trl peft accelerate bitsandbytes
!pip install torch torchvision torchaudio triton
!pip install xformers
!python -m xformers.info
!python -m bitsandbytes