The top 9 hugging face models & datasets measured by RiseFinder over the 7d window ending Week 42 · 12 October 2026.
Decision-2.0-Vega-27B is a 27B-parameter decision model that is part of Decision 2.0, the decision models of vLLM Semantic Router. It takes an input in text or JSON form along with questions to answer, such as picking one of several options, saying yes or no, or rating on a scale. It answers all of the questions at once and returns a probability for every answer.
Hugging Face model
Open source ↗EgoStandard is a video dataset from LightwheelAI. It is described as the 90,000-hour head-view line of EgoSuite-Open100K. The dataset pairs head-view egocentric video with synchronized 3D hand pose, and its body subset adds full-body pose. It is distributed through the LightwheelAI/EgoStandard Bucket, with a Git repository serving as the dataset card and access point for downloads.
Hugging Face dataset
Open source ↗This is a machine learning model repository on Hugging Face containing an EXL3 quantization of dealignai/GLM-5.3-UNCENSORED-FP8. That base model is a weight-edited variant, not a fine-tune, of zai-org/GLM-5.3, with the edit documented in a file named CRACK SURGERY.json. The repository states it is not affiliated with dealignai or Z.ai.
Hugging Face model
Open source ↗Origin Lab Game Recordings v0.4.0 is a dataset of human gameplay recorded inside the engines of 20 licensed games at 1080p and 60 FPS. Each recording includes RGB frames with and without the HUD, depth, surface normals, camera pose, keyboard and mouse input, in-engine events and game state, and audio. This release doubles the roster to 20 games and adds the first Unity title, with 169 sessions totaling 163.0 hours and a size of about 5.3 TB.
Hugging Face dataset
Open source ↗Taiga-S1 is a small, fast decision model designed for computer-use agents, where a planner decides what to do and a tiny model handles step-by-step execution. It is an experiment testing whether such models can be useful in this role, with FreeCAD serving as the testbed. The stated next step is applying the same approach to applications without a scripting API.
Hugging Face model
Open source ↗This is a conversational speech dataset. Basis Conversations 1500 contains multi-party, multilingual, full duplex conversations with up to four simultaneous speakers per conversation, each recorded on channel-separated 48 kHz audio. Conversations seat between two and four people, the median conversation lasts 33 minutes, and 2,645 unique speakers are represented.
Hugging Face dataset
Open source ↗biglam/europeana_newspapers_images is a dataset of 98,877 newspaper page images from the 1700s to the 1940s in 10 languages, plus pages marked multi-language or unidentified. Each page is paired with the OCR produced when it was digitised, including full text, ALTO XML, and line and word boxes converted to image pixels with the OCR engine's per-word confidence values.
Hugging Face dataset
Open source ↗GLiNER2.5-Decide is a 340M-parameter English text classification model in the GLiNER2.5 family. Instead of using a fixed prompt template, it accepts any label set at call time, covering uses such as intent, routing, sentiment, priority, policy, and multi-label tagging, all in a single forward pass without generating tokens. It can be loaded with AutoExtractor and run locally.
Hugging Face model
Open source ↗venvoo/china-a-share-l2-level2-limit-order-book-tick-data is a Hugging Face dataset containing a historical archive of Chinese exchange Level-2 market data from 2017 to 2026, supplied through a vendor export and stored in Parquet format. It includes ten-level quote snapshots, individual order messages, and trade-stream records, covering A-share stocks as well as non-stock instruments such as ETFs and bonds.
Hugging Face dataset
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