The top 5 Hugging Face models and datasets by download growth over the 1-day window ending 4 November 2026, measured by RiseFinder.
| Rank | Name | Before → after | Change |
|---|---|---|---|
| 1 | bitmanagerai-speech-lab/darbuka_arabic_tts_dataset | 1,160 → 1,507 downloads | +29.9% |
| 2 | LiquidAI/LFM3-Omni-2B | 9,964 → 11,662 downloads | +17% |
| 3 | RecursiveSI/flint | 29,574 → 32,677 downloads | +10.5% |
| 4 | hongzhuyi/VDEBench | 6,995 → 7,680 downloads | +9.8% |
| 5 | bones-studio/seed | 8,673 → 9,290 downloads | +7.1% |
This dataset is intended for research in the fields of speech recognition and text-to-speech applications. It compiles Arabic speech samples along with their corresponding automatic transcripts and audio-quality measurements. The data collection has a specific focus on capturing speech patterns from Egyptian and Saudi speakers.
Hugging Face dataset
Open source ↗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 ↗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 ↗VDEBench is a bilingual benchmark dataset for visual document editing, containing 3,506 examples split into 1,578 text edits and 1,928 table edits. Each example provides the input document image, an editing instruction, a target image, editing-region annotations, and recorded source provenance. Available masks indicate which regions of a document are allowed for editing.
Hugging Face dataset
Open source ↗bones-studio/seed, also called BONES-SEED (Skeletal Everyday Embodiment Dataset), is an open dataset of 142,220 annotated human motion animations intended for humanoid robotics. It contains motion capture data in SOMA and Unitree G1 formats, along with natural language descriptions, temporal segmentation, and detailed skeletal metadata.
Hugging Face dataset
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