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AI Models 84759

• moonshotai_Kimi-K2-Thinking-GGUF↗

  bartowski

• Z-Image-Turbo-GGUF-Collection↗

  BigDannyPt

• humo-gguf↗

  calcuis

• BSRoformer-GGUF↗

  chenmozhijin

  Official GGUF model repository for the BSRoformer.cpp project. This repository contains BS Roformer/Mel-Band-Roformer models converted to the GGUF format, enabling high-performance, cross-platform local inference using the MelBandRoformer.cpp inference engine. This repository contains GGUF quantized versions of the following original models: | Original Model | Original Author | Recommended (Q8_0) | All Versions | | :--- | :--- | :--- | :--- | | mel-band-roformer-deux | becruily | Download | View Models | | BS-RoFormer | anvuew | Download | View Models | | MelBandRoformers (voc_fv6) | GaboxR67 | Download | View Models | To meet different hardware requirements, various quantized versions are provided: - **q8_0** (Recommended): 8-bit quantization. Significantly reduces memory usage and bandwidth requirements while maintaining audio quality almost identical to FP32. - **fp16**: 16-bit floating point. Suitable for scenarios requiring maximum precision. - **q4_0 / q4_1 / q5_0 / q5_1**: Lower-bit quantization, suitable for devices with low VRAM/RAM. *(Note: Norm and Bias weights remain in FP32 to ensure numerical stability)* Please use with the BSRoformer.cpp command-line tool. Download the executable for your system from BSRoformer.cpp (or compile it yourself). Download the desired `.gguf` file (e.g., `voc_fv6-Q8_0.gguf`) using the direct links in the **Model List** above, or browse all models from the `Files and versions` page of this repository. ```bash ./bs_roformer-cli model_q8_0.gguf input.wav output.wav ./bs_roformer-cli model_q8_0.gguf input.wav output.wav --overlap 2

• claude-sonnet-4-6-thinking↗

  Anthropic

  Claude Sonnet 4.6 在各项基准测试中均有提升。它拥有接近 Opus 级别的智能，而价格却更实惠，使其能够胜任更多任务

• woof-textfiller-0.8B-4bit-v2.1↗

  ConwayResearch

• flan-t5-base-MNN↗

  darkmaniac7

• Qwen-Image-Bench-GGUF↗

  DawnW0lf

• IF-I-M-v1.0↗

  DeepFloyd

• man_woman_face_image_detection↗

  dima806

  Returns with about 98.7% accuracy whether the face belongs to man or woman based on face image. See https://www.kaggle.com/code/dima806/man-woman-face-image-detection-vit for more details.