Qwen3.6-35B-A3B, the first open‑source Qwen3.6 model, achieves markedly better scores than Qwen3.5‑35B‑A3B and Gemma4‑31B on Terminal‑Bench2.0, NL2Repo, and QwenClawBench, adds a thought‑process retention option, and is accessible via HyperAI’s ready‑to‑run notebook with free compute credits.
DeepTutor, an open‑source personal learning assistant from HKU’s Data Science Lab, combines multi‑agent collaboration, retrieval‑augmented generation, and web search to deliver end‑to‑end interactive learning—covering knowledge Q&A, visual explanations, exercise generation, and research support—while a step‑by‑step HyperAI tutorial shows how to deploy it with ready‑made compute resources.
HyperAI’s tutorial lets developers instantly launch the open‑source Gemma‑4‑31B model—supporting multimodal input, up to 256 K token context and over 140 languages—through a one‑click deployment on RTX 6000 or RTX 5090 GPUs, with detailed step‑by‑step instructions and optional compute credits.
This guide shows how to use HyperAI’s free CPU quota to quickly deploy popular open‑source LLMs such as Qwen3.5, DeepSeek‑R1, Gemma 3 and Llama 3.2, walking through environment setup, model download, and inference execution without needing local GPU hardware.
The article analyzes the fundamental challenges of 3D generative AI, compares existing NeRF, voxel, and single‑view methods, and explains how Microsoft’s open‑source TRELLIS.2 uses a field‑free O‑Voxel representation and 16× compression to produce 512³ full‑texture assets in about three seconds, with a step‑by‑step HyperAI demo.
The article introduces HuMo‑1.7B, a multimodal video generation framework that jointly processes text, reference images, and audio, achieves SOTA performance on several sub‑tasks, and provides a step‑by‑step tutorial for running the model on the HyperAI platform with detailed resource and parameter guidance.
Apple’s newly released SimpleFold model leverages flow‑matching and a pure Transformer architecture to eliminate costly MSA and triangular updates, achieving performance comparable to AlphaFold2 and RoseTTAFold2 on CAMEO22 and CASP14 benchmarks while dramatically reducing computational requirements, and a step‑by‑step tutorial lets users run it on HyperAI’s platform.
