This is an automatic speech recognition model optimized for Apple silicon chip devices. By converting to the MLX framework and quantizing to the FP8 format, it enables fast on-device speech transcription on Apple devices. The model is fine-tuned for verbatim accuracy and is particularly suitable for scenarios requiring high-precision transcription.

Audio ProcessingMlxOther

The 4-bit quantized version of VibeThinker-1.5B, optimized for Apple chips based on the MLX framework, is a dense language model with 1.5 billion parameters, specifically designed for mathematical reasoning and algorithm coding problems.

Natural Language ProcessingMlxMultiple Languages

This model is an MLX format conversion version of the instruction fine-tuned version of Falcon-H1-34B-Instruct, optimized specifically for Apple Silicon (M series chips). It is based on the original Falcon-H1-34B-Instruct model and converted to an 8-bit quantization format compatible with the MLX framework through the mlx-lm tool, aiming to achieve efficient local inference on macOS devices.

Natural Language ProcessingTransformersMultiple Languages

This model is based on the Kimi-Linear-48B-A3B-Instruct large language model of moonshotai and is converted into an 8-bit quantized version suitable for Apple Silicon chips (MLX framework) using the mlx-lm tool. It is an instruction fine-tuned model with 48B parameters, designed specifically for following human instructions and dialogue interaction.

Natural Language ProcessingMlx

Qwen3-VL-2B-Thinking is a vision-language model launched by Qwen. Based on a 2B parameter scale, it uses MLX for 8-bit quantization and is specifically optimized for Apple Silicon chips. This model supports multimodal understanding and generation tasks for images and text.

MultimodalTransformers

Granite-4.0-H-1B-8bit is a small language model in the IBM Granite series, specifically optimized for Apple Silicon chips. It uses 8-bit quantization technology, has 1B parameters, and features efficient inference and low resource consumption.

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

DeepSeek-OCR-6bit is an MLX format version converted from the DeepSeek-OCR model, specifically optimized for Apple chips. This is a vision-language model with powerful optical character recognition capabilities, capable of extracting and recognizing text information from images.

MultimodalMlxOther

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-32B-Instruct is a powerful multimodal vision-language model developed based on the Qwen foundation model. After optimization and quantization, it is specifically optimized for Apple chip devices, providing efficient image-text processing capabilities.

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

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

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

This is an MLX format version converted from the GLM-4.6 model, using 8-bit quantization technology with a group size of 32, optimized for Apple chips, and providing efficient text generation capabilities.

Natural Language ProcessingMlxMultiple Languages

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

Apriel-1.5-15B-Thinker is a 15-billion-parameter multimodal model designed for image understanding and reasoning. It uses the mid-term training method instead of RLHF training. This version is an MLX quantized version suitable for Apple chips, featuring low memory usage and fast startup speed.

MultimodalMlxEnglish