# Multi-framework compatibility

A dual-framework compatible vision model trained on MetaCLIP-2.5B dataset, supporting zero-shot image classification tasks

Vision Transformer model trained on MetaCLIP-400M dataset, supporting zero-shot image classification tasks

A dual-framework compatible vision model trained on the MetaCLIP-2.5B dataset, supporting both OpenCLIP and timm frameworks

A vision Transformer model trained on the MetaCLIP-2.5B dataset, compatible with both open_clip and timm frameworks

A distilled version of Whisper optimized for French speech-to-text, retaining only 2 decoder layers to improve inference efficiency while maintaining accuracy

Mythalion Kimiko v2 - AWQ is the AWQ quantized version of the Mythalion Kimiko v2 model created by nRuaif, featuring high efficiency, accuracy, and fast inference.

The GPTQ quantized version of Mistral 7B Instruct v0.1, supporting operation under the ExLlama or Transformers framework