The top 5 hugging face models & datasets measured by RiseFinder over the 1d window ending 28 October 2026.
OvisOCR3 is a unified 0.8B multimodal model designed for OCR and document understanding. This compact model integrates several functions into a single unit. It performs document parsing, general OCR, key information extraction, document figure captioning, and cross-page table reconstruction. These capabilities are selected through task-specific methods.
Hugging Face model
Open source ↗Flint is a mixture-of-experts language model made by Recursive for use with coding agents. It has 39.8 billion total parameters with approximately 2.5 billion active per token. It is designed for software engineering work such as investigating codebases, fixing bugs, implementing features, and writing tests.
Hugging Face model
Open source ↗FastVideo/FastH3-Omni-Comfy is a set of ComfyUI-ready BF16 and INT8 ConvRot diffusion models from the final FastH3 Omni release. The models generate video together with synchronized audio from a prompt plus an ordered set of image, video, and audio references. They are packaged for use within the ComfyUI workflow tool.
Hugging Face model
Open source ↗AgiBot World 2026 is a real-world embodied intelligence dataset published on Hugging Face for robotics research. It is collected from real-world environments including commercial spaces and home scenarios using the AGIBOT G2 robot platform in a free-form collection mode, and provides structured, accurately annotated data for developing embodied agents. It is organized as a LeRobot dataset, with Parquet episode files, MP4 video streams per camera, and metadata defined in info.json. Simulation data collected via a digital twin is concurrently open-sourced in the GenieSim project.
Hugging Face dataset
Open source ↗A speech recognition model for English. Bark 3.0 is a 0.6 billion parameter English model distributed as a 163 MB download, and it averages 4.94 percent word error across the Open ASR Leaderboard's seven English evaluation sets. Its small size and reported accuracy make it applicable to English automatic speech recognition tasks where download size matters.
Hugging Face model
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