# Apple chip optimization

An 8-bit quantized version based on the DeepSeek-R1-0528-Qwen3-8B model, optimized for Apple Silicon chips and suitable for text generation tasks.

A large language model developed by DeepSeek AI, optimized with 4-bit quantization, suitable for Apple chip devices.

The 4-bit AWQ quantized version of Qwen3-8B, optimized for the MLX library, supports 192k token long context processing, suitable for edge device deployment.

This is a 3-bit deep quantized model converted from Qwen/Qwen3-235B-A22B, suitable for text generation tasks.

4-bit quantized version of VL-Rethinker-72B, optimized for Apple devices using the MLX framework, supporting visual question answering tasks.

VL-Rethinker-7B 4-bit MLX Quantized Version is a quantized variant of the TIGER-Lab/VL-Rethinker-7B model, optimized for Apple devices and supporting visual question-answering tasks.

Rombo-LLM-V3.1-QWQ-32b is a 32B-parameter large language model, processed with llama.cpp's imatrix quantization, offering multiple quantization versions to accommodate different hardware requirements.

A 4-bit quantized version based on deepseek-ai/Janus-Pro-7B, optimized for Apple chip devices and suitable for chat tasks.

4-bit quantized version based on deepseek-ai/Janus-Pro-1B, optimized for Apple chips using the MLX framework

An 8-billion-parameter brand safety classification model optimized based on Llama 3.1, specifically designed for Apple chip devices

MLX framework version optimized from Stable Diffusion 3.5 Large, specifically designed for Apple chip-optimized text-to-image generation models

MLX implementation of Stable Diffusion 3 Medium, focused on text-to-image generation

A text-to-image latent diffusion model optimized for Apple chips in Core ML format

Text-to-image model based on latent diffusion architecture, supporting English prompts to generate high-quality images

Text-to-image generation technology based on the latent diffusion model, capable of generating realistic images according to text input