This is a version of the Qwen3-Next-80B large language model after 4-bit quantization using Huawei's SINQ (Sinkhorn Normalized Quantization) method. SINQ is a novel, fast, and high-quality quantization method designed to significantly compress the model size (by approximately 75%) while maintaining almost the same accuracy as the original model, making it easier to deploy.

Natural Language ProcessingSafetensorsEnglish

Qwen3-Next-80B-A3B-Thinking is a quantized version based on the Qwen3-Next-80B-A3B-Thinking model, specifically optimized for text generation tasks. This model needs to be used in conjunction with version PR 16095 of llama.cpp and offers multiple quantization levels to adapt to different hardware configurations.

Natural Language ProcessingGguf

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 the MXFP4 quantized version of Qwen3-Next-80B-A3B-Thinking, which uses advanced quantization technology to significantly reduce storage and computational requirements while maintaining model performance. As an experimental version, it demonstrates the latest achievements in quantization technology.

Natural Language ProcessingGguf

This is an MXFP4 quantized experimental version of Qwen3-Next-80B-A3B-Instruct, developed based on cutting-edge quantization technology, providing efficient text generation capabilities. This version is experimental and requires a special version of llama.cpp support.

Natural Language ProcessingGguf

Qwen3-Next-80B-A3B-Thinking-1M-qx64n-mlx is a large language model that adopts a mixture-of-experts architecture and Deckard mixed precision quantization technology. It has 80 billion parameters and a context length of 1M tokens. This model performs excellently in science and technology reasoning and long text processing, showing a 20 - 35% improvement in cognitive benchmark tests compared to instruction models.

Natural Language ProcessingMlx

An instruction fine-tuning model with 80 billion parameters based on Qwen3-Next, using Deckard qx64n mixed-precision quantization technology, supporting a context length of 1 million, and performing excellently in abstract reasoning, memory efficiency, and long-context processing.

Natural Language ProcessingMlx

This is the GGUF quantized format version of the Qwen3-Next-80B-A3B-Instruct model, provided by lefromage. This model is a large language model with 80 billion parameters, licensed under the Apache 2.0 license, and supports text generation tasks. The GGUF format facilitates deployment and operation on local devices.

Natural Language ProcessingGguf

Qwen3-Next-80B-A3B-Thinking-1M-qx86n-hi-mlx is an MLX format version converted from the Qwen3-Next-80B-A3B-Thinking model, specifically excelling in long-chain reasoning and step-by-step thinking, and performing outstandingly in reasoning tasks.

Natural Language ProcessingMlx

This is a Q4_0 quantized version based on the Qwen3-Next-80B-A3B-Instruct model. Through specific quantization techniques, the efficiency of the model in storage and computation has been significantly improved while maintaining good performance.

Natural Language ProcessingGguf

Qwen3-Next-80B-A3B-Thinking is a new-generation thinking large language model launched by the Tongyi Qianwen team. It adopts an innovative hybrid attention mechanism and a highly sparse MoE architecture. While maintaining efficient reasoning, it has powerful complex reasoning capabilities and natively supports a context length of 262K.

Natural Language ProcessingTransformers

This is a version after quantizing Qwen/Qwen3-Next-80B-A3B-Thinking using LLM Compressor and the NVFP4 format. It adopts the FP4 micro-scaling technology, significantly reducing model storage and computational requirements while maintaining high precision.

Natural Language ProcessingTransformersMultiple Languages

Qwen3-Next-80B-A3B-Instruct is a cutting-edge large language model in the Qwen3-Next series. It improves parameter efficiency and inference speed through an innovative architecture and demonstrates excellent performance in multiple domains. It natively supports long context processing.

Natural Language ProcessingTransformers

Qwen3-Next-80B-A3B-Instruct is an efficient sparse mixture of experts model developed by the Tongyi Qianwen team. It has a total of 80 billion parameters, with only 3 billion active parameters. The model adopts an innovative hybrid attention mechanism and a MoE architecture with an extremely low activation rate, significantly improving inference efficiency while maintaining strong performance. It natively supports a context length of 262,000 tokens and can be extended to 1 million tokens.

Natural Language ProcessingTransformers

This is the 8-bit AWQ quantized version of the Qwen3-Next-80B-A3B-Thinking model. It optimizes inference efficiency through advanced quantization technology, significantly reducing resource consumption while maintaining model performance. It is suitable for large-scale text generation tasks.

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

Qwen3-Next-80B-A3B-Thinking is an 80B parameter large language model that has undergone 4-bit quantization and is specifically optimized for Apple Silicon. It has been selected for the LM Studio Community Model Highlights Program and focuses on text generation tasks.

Natural Language ProcessingTransformers

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

Qwen3-Next-80B-A3B-Instruct is a large-scale language model developed by the Tongyi Qianwen team of Alibaba Cloud. It has been optimized with 4-bit quantization using the MLX framework and specifically optimized for Apple chip devices to provide efficient inference capabilities.

Natural Language ProcessingTransformers

This is a 4-bit mxfp4 quantized version optimized for Apple MLX based on the Qwen3-Next-80B-A3B-Thinking base model. It is specifically optimized for Apple silicon chips and can achieve efficient local inference on Mac devices, significantly reducing memory usage while maintaining good performance.

Natural Language ProcessingMlx