This is the MXFP4_MOE quantized version of the Ling-flash-2.0 model, specifically optimized for text generation tasks. This version uses the MXFP4_MOE quantization technology to significantly reduce the model size while maintaining the model's performance and improve the inference efficiency.

Natural Language ProcessingGguf

This is the Llamacpp imatrix quantized version of the Ling-flash-2.0 model by inclusionAI. Through advanced quantization technology, it significantly reduces memory usage and computational requirements while maintaining model performance, thereby improving operational efficiency. It supports multiple quantization levels and is suitable for different hardware configurations.

Natural Language ProcessingGguf

The Ming-flash-omni preview version is a multimodal large model built on the Ling-Flash-2.0 sparse mixture of experts (MoE) architecture, with a total of 100B parameters and only 6B parameters activated per token. This model is a comprehensive upgrade based on Ming-Omni, showing significant improvements in multimodal understanding and generation, especially in speech recognition, image generation, and segmentation editing.

MultimodalDiffusersEnglish