This is the MLX format conversion version of the MiniMax-M2 model, converted from the original model using mlx-lm 0.28.1. It supports 8-bit quantization and an optimized configuration with a group size of 32, and is optimized for running on Apple Silicon devices.

Natural Language ProcessingMlxEnglish

This is the 8-bit quantized version of the MiniMax-M2 model in MLX format, converted from the original model using mlx-lm 0.28.4 and optimized for Apple Silicon devices.

Natural Language ProcessingMlx

MiniMax-M2-6bit is the MLX format conversion version of the MiniMaxAI/MiniMax-M2 model, converted using mlx-lm 0.28.4, and supports efficient operation on Apple Silicon devices.

Natural Language ProcessingMlx

MiniMax-M2-4bit is a 4-bit quantized version converted from MiniMaxAI/MiniMax-M2 using the mlx-lm tool. It is specifically optimized for Apple Silicon chips and provides efficient text generation capabilities.

Natural Language ProcessingMlx

DeepSeek-OCR-8bit is an MLX format version converted from the DeepSeek-OCR model. It is a visual language model specifically optimized for Apple chips, supporting multilingual OCR recognition and image text understanding tasks.

MultimodalMlxOther

This is the MLX format conversion version of the Qwen3-Next-80B-A3B-Instruct model, optimized for efficient operation on Apple Silicon devices. The model is a large language model with 80 billion parameters, supporting text generation tasks and having powerful dialogue and reasoning capabilities.

Natural Language ProcessingMlx

This is a mixture-of-experts model based on the GLM-4.6 architecture. The REAP method was used to uniformly prune 40% of the experts. The model has 218B parameters and has been converted to a 3-bit quantization version in the MLX format, which is suitable for efficient operation on Apple chip devices.

Natural Language ProcessingTransformersEnglish

Qwen3-VL-2B-Instruct is an efficient image-text-to-text model developed by the Qwen team. This model is optimized with MLX 8-bit quantization and is particularly suitable for Apple Silicon devices. It can handle visual language tasks and provide efficient solutions.

MultimodalTransformers

This model is a mixture of experts model after 25% uniform pruning of GLM-4.5-Air using the REAP method. It has been converted to a 4-bit quantized version in the MLX format, suitable for efficient inference on Apple chip devices.

Natural Language ProcessingTransformersEnglish

This is an MLX format version converted from the Qwen3-Coder-REAP-25B-A3B model, converted from the original model using the mlx-lm tool, specifically optimized for Apple Silicon chips, and supports efficient text generation tasks.

Natural Language ProcessingMlxEnglish

An optimized version based on the GLM-4.5-Air model with 25% expert pruning using the REAP method, converted to the MLX format for efficient operation on Apple Silicon devices

Natural Language ProcessingMlxEnglish

This is the 4-bit quantized version of the Qwen3-VL-4B-Instruct model, specifically optimized for Apple Silicon chips and converted using the MLX framework. This model is a vision-language model that supports image understanding and multimodal dialogue tasks.

MultimodalMlx

Qwen3-VL-8B-Instruct is a multimodal vision-language model developed by the Qwen team, which supports the conversion from image-text to text. This version has undergone MLX 8-bit quantization processing and is specifically optimized for Apple silicon chips, improving the operating efficiency while maintaining performance.

MultimodalMlx

Qwen3-VL-8B-Instruct is a visual-language model developed by Qwen, optimized through MLX quantization, and specifically designed for Apple Silicon devices. This model supports multi-modal input of images and text, and can understand and generate text content related to images.

MultimodalMlx

LFM2-8B-A1B is an 8-bit quantized MLX build version optimized for Apple Silicon chips, adopting the Mixture of Experts (MoE) architecture. It has approximately 8 billion total parameters, with about 1 billion parameters activated per token, and supports fast inference on the device side.

Natural Language ProcessingMlxEnglish

ethicalabs/granite-4.0-h-small-base-MLX is an MLX format version converted from the IBM Granite-4.0-H-Small-Base model, specifically optimized for the Apple MLX framework to provide efficient text generation capabilities.

Natural Language ProcessingMlx

The 5-bit quantized version of the IBM Granite-4.0-H-Tiny model, optimized for Apple silicon chips. It uses a hybrid architecture of Mamba-2 and soft attention, combined with a Mixture of Experts model (MoE), to achieve efficient inference while maintaining high quality.

Natural Language ProcessingMlxMultiple Languages

IBM Granite-4.0-H-Tiny is a hybrid Mamba-2/Transformer model optimized for Apple silicon chips, using 3-bit quantization technology, and is designed for long context, efficient inference, and enterprise use. This model combines the Mamba-2 architecture with the Mixture of Experts (MoE) technology, significantly reducing memory usage while maintaining expressiveness.

Natural Language ProcessingMlxMultiple Languages

This is a 4-bit quantized version of the IBM Granite-4.0-h-Tiny model, optimized for Apple Silicon and using the MLX framework for efficient inference. The model has been processed with DWQ (Dynamic Weight Quantization) to significantly reduce the model size while maintaining performance.

Natural Language ProcessingMlx

This is the 8-bit quantized version of the IBM Granite-4.0-H-Micro model in MLX format, optimized for Apple Silicon, providing efficient large language model inference capabilities.

Natural Language ProcessingMlx