This is a quantized version of the VibeStudio MiniMax-M2-THRIFT model, generated using the llama.cpp tool and a specific dataset. It provides GGUF files of various quantization types and supports running in LM Studio or projects based on llama.cpp.

Natural Language ProcessingGguf

This is a large language model with 172B parameters obtained by uniformly pruning 25% of the experts in MiniMax-M2 using the REAP method. It has been optimized and quantized specifically for llama.cpp, supporting multiple quantization levels and can run in LM Studio or projects based on llama.cpp.

Natural Language ProcessingGgufEnglish

This is a large language model with 139B parameters obtained by uniformly pruning 40% of the experts in MiniMax-M2 using the REAP method. It adopts the GLM architecture and Mixture of Experts (MoE) technology, and undergoes various quantization processes through llama.cpp, suitable for text generation tasks.

Natural Language ProcessingGgufEnglish

This project provides a quantized version of the cerebras/MiniMax-M2-REAP-172B-A10B model, aiming to make knowledge accessible to the public. This is a large language model with 172 billion parameters, which has been optimized and quantized to reduce deployment costs and improve inference efficiency.

Natural Language ProcessingGguf

This is the MXFP4_MOE quantized version of the MiniMax-M2-REAP-172B-A10B model, which is a memory-efficient compressed model. Through the REAP (Routing-Weighted Expert Activation Pruning) method, the model is compressed from 230B parameters to 172B parameters while maintaining performance, with a 25% reduction in size. It is suitable for resource-constrained environments, local deployment, and academic research.

Natural Language ProcessingGguf

MiniMax-M2-REAP-162B-A10B is an efficiently compressed version of MiniMax-M2. It uses the REAP (Routing Weighted Expert Activation Pruning) method to reduce the model size by 30% while maintaining almost the same performance, compressing from 230B parameters to 162B parameters, significantly reducing the memory requirement.

Natural Language ProcessingTransformersEnglish

MiniMax-M2-REAP-172B-A10B is a memory-efficient compressed variant of MiniMax-M2. It uses the REAP expert pruning method, reducing the model size by 25% from 230B parameters to 172B parameters while maintaining almost the same performance.

Natural Language ProcessingTransformersEnglish

MiniMax-M2 AWQ - INT4 is a quantized version based on the MiniMax-M2 model. It uses INT4 quantization technology to significantly reduce memory usage and improve inference efficiency while ensuring performance. This model performs excellently in coding and agent tasks and has outstanding comprehensive performance.

Natural Language ProcessingTransformers

This is the MXFP4_MOE quantized version of the MiniMax-M2-THRIFT model. It has been compressed based on the original model, including 25% expert pruning (from 256 to 192) and setting top_k=8. Meanwhile, it retains the characteristics of the encoding model and can be used for text generation tasks.

Natural Language ProcessingGguf

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

MiniMax-M2 is a small Mixture of Experts (MoE) model built specifically for maximizing coding and agent workflows. It has a total of 230 billion parameters, with 1 billion active parameters. This model excels in coding and agent tasks while maintaining strong general intelligence. It is compact, fast, and cost-effective.

Natural Language ProcessingTransformers

This project is a high-quality HQ4_K quantization of the MiniMax-M2 model, optimized specifically for text generation tasks and particularly suitable for dialogue scenarios. This quantized version does not use imatrix and maintains the model's performance.

Natural Language ProcessingGguf

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 project quantizes the MiniMax-M2 model of MiniMaxAI. Using the llama.cpp tool, it provides model files of multiple quantization types for users with different needs, facilitating the efficient operation of the model under different hardware conditions.

Natural Language ProcessingGguf

This project is a quantized version based on the MiniMaxAI/MiniMax-M2 model, aiming to make knowledge accessible to the public. It provides multiple model versions with different quantization levels and shows the perplexity performance indicators of each version.

Natural Language ProcessingGguf

MiniMax-M2 is a Mixture of Experts model designed for efficient coding and agent workflows. It has a total of 230 billion parameters and 10 billion active parameters. This model performs excellently in coding and agent tasks, and features low latency, low cost, and high throughput, effectively improving work efficiency.

Natural Language ProcessingTransformers

This is the MXFP4_MOE quantized version of the MiniMax-M2 model. It is re-quantized based on the version with the chat template fixed by unsloth, enabling more efficient use of the MiniMax-M2 model's capabilities in specific scenarios. This is a coding model that needs to be used with the latest llama.cpp.

Natural Language ProcessingGguf

MiniMax-M2 is a small Mixture of Experts (MoE) model built specifically for maximizing coding and agent workflows. It has a total of 230 billion parameters, with only 10 billion parameters activated. It performs excellently in coding and agent tasks while maintaining strong general intelligence, featuring compactness, speed, and cost-effectiveness.

Natural Language ProcessingTransformers

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

MiniMax-M2 6.5bit MLX is a quantized version based on the MiniMax-M2 model, which performs excellently in text generation tasks and supports multiple quantization levels. Among them, the q6.5bit quantization can achieve a perplexity of 1.128 in testing, which is comparable to q8.

Natural Language ProcessingMlx