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AI 모델 84759

• MaskGCT↗

  amphion

• Race-Classification-FairFace-YOLOv8↗

  Anzhc

  This model is based on FairFace 0.25 padding variant dataset composed by Microsoft researchers, aiming to reduce bias by better balancing classes in dataset. > **Karkkainen, Kimmo, and Joo, Jungseock.** > *FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation.* > Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2021, pp. 1548–1558. > FairFace Dataset on GitHub This YOLOv8 training is meant only for race classification. I wanted a really really fast model for tagging, and this is likely what it's useful for! ~~I will provide a pipeline for running it on your datasets in future.~~ I've made simple scripts for you to use on your data. By default it will output .txt files(or append to existing), so modify for your specific needs: https://github.com/Anzhc/Simple-Utility-Scripts-for-YOLO/tree/main

• APUS-Reflex-35B-A3B-GGUF↗

  apus-ailab

  > **Branding note:** This model family is now **APUS Reflex**. Some weight filenames, script/module names, served-model aliases, and the separate evaluation dataset ID retain their historical identifiers for download and runtime compatibility; these do not indicate different model versions. GGUF conversions of APUS-Reflex-35B-A3B (revision `494738529137`) for **Ollama, llama.cpp and LM Studio** on Linux, Windows and macOS (Metal). APUS Reflex is a **decision model**: each request supplies a state, an instruction and 2–16 candidates; the model answers with one candidate label (A–P) and the application reads the distribution over those labels. It is not a chat model.

• WeSpeaker-ResNet34-LM-CoreML↗

  aufklarer

• google_gemma-3-270m-it-GGUF↗

  bartowski

• openai_gpt-oss-20b-GGUF↗

  bartowski

  All quants keep the feed forward networks at mxfp4 for optimal performance, which does mean the size differences are negligible unfortunately, but being provided just because. No chat template specified so default is used. This may be incorrect, check original model card for details. The reason is, the FFN (feed forward networks) of gpt-oss do not behave nicely when quantized to anything other than MXFP4, so they are kept at that level for everything.

• beetle-bilingual-l2-50-simultaneous-b2-fineweb-2b-jpn-eng↗

  Beetle-FineWeb-24B-4

• Bespoke-Nimble-9B-v2↗

  bespokelabs

• Qwen-Image-2.1-viggle-turbo↗

  Cdimabusinessent

• sdxs-512-tinySDdistilled-GGUF↗

  concedo

  Quantized to GGUF from https://huggingface.co/akleine/sdxs-512 based on https://huggingface.co/IDKiro/sdxs-512-dreamshaper