The top 5 hugging face models & datasets measured by RiseFinder over the 1d window ending 16 October 2026.
This repository hosts a GGUF quantized version of MiniCPM-V 4.7 35B-A3B, a model from OpenBMB. GGUF is a format used with llama.cpp, and quantization reduces the model's size so it can run on more modest hardware. The original BF16 weights and the full model card are available in the separate openbmb/MiniCPM-V-4.7-35B-A3B repository.
Hugging Face model
Open source ↗basis-ai/basis-global-emotions is a speech dataset of acted emotional speech. It contains 525.17 hours of audio across 137,696 recordings, 54,575 series, 10,420 speakers and 108 locales. Each recording is one native speaker performing a line in a described situation according to a steer, which is an emotion such as happy or frustrated or a direction such as whispering or screaming. Recordings are organized into series, where the same speaker reads the same line multiple times.
Hugging Face dataset
Open source ↗MiniCPM-V 4.7 is a vision-language model from the MiniCPM-V series, released in a 1B variant. It handles single-image, multi-image, and video understanding, and combines foundation capabilities with efficient visual encoding and on-device inference, meaning it can run on local devices rather than only in the cloud.
Hugging Face model
Open source ↗This is a machine learning dataset containing a selected code corpus of approximately 240 billion tokens covering 11 programming languages from The Stack v3. The data was produced through quality-control cleaning and fine-grained selection using the UltraData-Code-L2-Classifier, applying a language-adaptive L2 selection framework.
Hugging Face dataset
Open source ↗Cactus-Compute/whistle is a speech-to-text model designed for mobiles, wearables, robots, smart home devices, automotive systems, and microcontrollers. The whole model is a single 16.9 MB file. It runs on the same CPU engine as Needle, from the same container and the same quantisation, with no dependencies and no GPU.
Hugging Face model
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