The top 5 hugging face models & datasets measured by RiseFinder over the 1d window ending 2 November 2026.
LiquidAI/LFM3-Omni-2B is an AI model that handles multiple kinds of input and output in a single system. It understands text, images, video and audio, and responds in text or speech. It is built for on-device use, replacing what would otherwise require a separate ASR model, vision-language model, LLM and TTS model.
Hugging Face model
Open source ↗This dataset serves as a benchmark for referring expression segmentation. While it has historically been a standard benchmark, the evidence notes that it contains issues such as poor mask quality and harmful referring expressions. Furthermore, modern models are capable of producing masks that are more accurate than the ground-truth annotations provided in the dataset, which suggests the dataset may be an imprecise measure.
Hugging Face dataset
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 ↗Vyber07/cyber-security is a cybersecurity knowledge base dataset intended for AI training. It covers cybersecurity domains including offensive red teaming, bug bounty exploitation, defensive SOC operations, digital forensics, and AI/LLM security. The dataset is 16 GB in size, contains 507 files, and spans more than 30 domains drawn from more than 15 platforms.
Hugging Face dataset
Open source ↗jialinyyzz/humanizer is a 12B language model that rewrites AI-written drafts such as emails, essays, reports, and forum posts in English and Chinese so they read like a person wrote them. It is trained to keep every number, unit, date, name, and quote from the original text and to add nothing. The model runs locally, and no AI detector was used anywhere in its training.
Hugging Face model
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