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

Qwen3-VL is the most powerful vision-language model in the Tongyi large model series. It has excellent text understanding and generation capabilities, in-depth visual perception and reasoning capabilities, long context support, strong spatial and video dynamic understanding capabilities, and outstanding intelligent agent interaction capabilities. This model uses a Mixture of Experts (MoE) architecture and is a thinking version for enhanced reasoning.

MultimodalGguf

Snowflake Arctic is a large language model developed by Snowflake AI Research Team, featuring a dense Mixture of Experts (MoE) architecture with 480 billion parameters, specifically designed for efficient text and code generation.

Natural Language ProcessingTransformers

DBRX Base is a large language model based on the Mixture of Experts (MoE) architecture, trained from scratch by Databricks, using an open license agreement. The model has a total of 132B parameters, with only 36B parameters activated under any input. It was pre-trained on 12T of text and code data, with a maximum context length of 32K tokens.

Natural Language ProcessingTransformers

FusionNet_34Bx2_MoE is a large language model that adopts the Mixture of Experts (MoE) architecture, with 60.8 billion parameters. It is fine-tuned on English corpora and can significantly improve text generation performance, providing high-quality solutions for various natural language processing tasks.

Natural Language ProcessingTransformersEnglish

Mixtral MOE 2x10.7B is a large language model based on the Mixture of Experts architecture, which combines the advantages of two base models, Sakura-SOLAR-Instruct and CarbonVillain. This model performs excellently in text generation tasks and has been evaluated on multiple public datasets, including benchmark tests such as AI2 Reasoning Challenge, HellaSwag, and MMLU.

Natural Language ProcessingTransformers