This model is an MLX format conversion version of the Ministral-3-3B-Instruct-2512 instruction fine-tuning model released by Mistral AI. It is a large language model with a parameter scale of 3B, specifically optimized for following instructions and dialogue tasks, and supports multiple languages. The MLX format enables it to run efficiently on Apple Silicon devices.

Natural Language ProcessingMlxMultiple Languages

This model is an MLX format conversion version of Kimi-Linear-48B-A3B-Instruct, optimized for Apple Silicon devices such as the Apple Mac Studio. It is a large language model with 48 billion parameters, supporting instruction following and suitable for local inference and conversation tasks.

Natural Language ProcessingMlx

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

This model is an 8-bit quantized version converted from allenai/Olmo-3-7B-Instruct, specifically optimized for the Apple MLX framework. It is a large language model with 7 billion parameters, supporting instruction following and dialogue tasks.

Natural Language ProcessingMlxEnglish

This is an optimized version based on the original DeepSeek-OCR model, specifically an OCR model that supports inference on Apple Metal Performance Shaders (MPS) and CPU. It can extract text from images and convert it into a structured format, supporting multilingual document recognition.

MultimodalTransformersOther

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 model converted from summykai/gemma3-27b-abliterated-dpo using version 0.28.3 of mlx-lm. It is a 27B parameter Gemma 3 large language model fine-tuned with DPO (Direct Preference Optimization), optimized for efficient operation on Apple Silicon (MLX framework).

Natural Language ProcessingTransformersEnglish

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

A text generation model implemented based on the MLX library, supporting inference in multiple quantization methods, with distributed computing capabilities, and can run efficiently in the Apple hardware environment.

Natural Language ProcessingMlxEnglish

This is a text-to-speech model optimized based on the MLX framework, converted from the original model Marvis-AI/marvis-tts-100m-v0.2. It uses 6-bit quantization technology and is specifically optimized for Apple Silicon hardware, providing efficient speech synthesis capabilities.

Audio ProcessingTransformersMultiple Languages

This is a 6-bit quantized version converted from the Kimi-Linear-48B-A3B-Instruct model, optimized for the Apple MLX framework. The model retains the powerful instruction-following ability of the original model, while significantly reducing storage and computational requirements through quantization technology, making it suitable for efficient operation on Apple hardware.

Natural Language ProcessingMlx

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

Qwen3-VL-2B-Thinking is one of the most powerful vision-language models in the Qwen series. It uses GGUF format weights and supports efficient inference on devices such as CPUs, NVIDIA GPUs, and Apple Silicon. This model has excellent multimodal understanding and reasoning capabilities, especially enhancing visual perception, spatial understanding, and agent interaction functions.

MultimodalTransformers

This is a 4-bit quantized version converted from the moonshotai/Kimi-Linear-48B-A3B-Instruct model, optimized for the Apple MLX framework, providing efficient text generation capabilities.

Natural Language ProcessingMlx

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

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-M2-6bit 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