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

Kimi-K2-Thinking is a large language model in MLX format converted by mlx-community from the original model of moonshotai. The conversion is carried out using version 0.28.4 of mlx-lm, retaining the chain of thought reasoning ability of the original model.

Natural Language ProcessingMlx

This project performs 4-bit quantization on the MiniMax-M2 model using the DWQ (Dynamic Weight Quantization) method with the help of the mlx-lm library. This model is a lightweight version of MiniMax-M2, significantly reducing the model size while maintaining good performance.

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

This project is the result of quantizing the MiniMax-M2 model to 3 bits using the mlx-lm library through Dynamic Weight Quantization (DWQ). It can efficiently perform text generation tasks under resource-constrained conditions, providing a more lightweight solution for related applications.

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

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

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 an 8-bit quantized version of the OpenAI GPT-OSS-20B model in MLX format, converted using mlx-lm 0.28.2 and optimized for Apple Silicon, providing efficient text generation capabilities.

Natural Language ProcessingTransformers

This is a lightweight version of the ByteDance Seed-OSS-36B-Instruct model quantized to 4 bits using DWQ. It is distilled from the BF16 teacher model using mlx-lm 0.27.1 and supports Chinese-English bilingual text generation tasks.

Natural Language ProcessingMlxMultiple Languages

This is the MLX format conversion version of the Qwen3-Omni-30B-A3B-Thinking model, converted using mlx-lm 0.28.1, and supports efficient operation on Apple Silicon devices.

Natural Language ProcessingMlxEnglish

This is an MLX format version converted from the Qwen3-Omni-30B-A3B-Instruct model, converted using the mlx-lm 0.28.1 tool, optimized for Apple Silicon, and supports efficient text generation tasks.

Natural Language ProcessingMlxEnglish

This is the 5-bit quantization version of the GLM-4.6 model, converted using the mlx-lm tool and optimized for Apple Silicon devices, providing efficient large language model inference capabilities.

Natural Language ProcessingMlxMultiple Languages

GLM-4.6-4bit is a 4-bit quantized version converted from the zai-org/GLM-4.6 model using the mlx-lm tool. It is specifically optimized for Apple Silicon chips and supports Chinese and English text generation tasks.

Natural Language ProcessingMlxMultiple Languages

This model is an MLX format version converted from the Kimi-K2-Instruct-0905 model of moonshotai using the mlx-lm 0.28.0 version. It adopts 3-bit quantization technology with a group size of 32. On average, each weight only occupies 4.002 bits, which optimizes the running efficiency of the model on Apple devices.

Natural Language ProcessingTransformers

This is the 3-bit quantized MLX format version of the Qwen3-Next-80B-A3B-Thinking model, optimized for Apple Silicon devices. It was converted using the mlx-lm 0.27.1 tool, maintaining the strong inference ability of the original model while significantly reducing the model size.

Natural Language ProcessingMlx

This model is a 4-bit quantized MLX format model converted from the original Qwen/Qwen3-Next-80B-A3B-Instruct model using mlx-lm version 0.28.0. It is optimized for Apple chips and supports efficient text generation tasks.

Natural Language ProcessingMlx