This is an MXFP4_MOE imatrix quantized version based on the Huihui-MiroThinker-v1.0-30B model, specifically optimized for text generation tasks. It significantly reduces the model size and inference cost while maintaining model performance.

Natural Language ProcessingGguf

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 GLM-4.5-Air-REAP-82B-A12B model, specifically optimized for text generation tasks. This model is based on the GLM-4.5-Air-REAP-82B-A12B base model of Cerebras and is implemented through MXFP4 mixed expert quantization technology, significantly reducing the model size and inference cost while maintaining performance.

Natural Language ProcessingGguf

NVIDIA-Nemotron-Nano-VL-12B-V2-FP4-QAD is an autoregressive vision-language model launched by NVIDIA. Based on an optimized Transformer architecture, it can handle both image and text inputs simultaneously. The model uses FP4 quantization technology, which significantly reduces the model size and inference cost while maintaining performance, and is suitable for various multimodal application scenarios.

MultimodalTransformers

Qwen3-Nemotron-32B-RLBFF is a large language model fine-tuned based on Qwen/Qwen3-32B. The quality of the model's generated responses in the default thinking mode has been significantly improved through reinforcement learning feedback technology. This model performs excellently in multiple benchmark tests while maintaining low inference costs.

Natural Language ProcessingTransformersEnglish

This is an MXFP4 quantized version based on Huihui-gpt-oss-20b-BF16-abliterated-v2, specifically designed for text generation tasks. The model uses MXFP4 quantization technology, which significantly reduces the model size and inference cost while maintaining performance. It supports multiple deployment methods, including QAT, ollama, and GGUF formats.

Natural Language ProcessingTransformers

This project is the A3B quantized version of Alibaba's Tongyi In-depth Research 30B model. It aims to reduce the model deployment cost through quantization technology and make knowledge accessible to everyone. This model is optimized based on a large language model with a scale of 30B parameters, maintaining the strong capabilities of the original model while improving the inference efficiency.

Natural Language ProcessingGguf

MobileLLM-R1 is an efficient inference model in the MobileLLM series, specifically optimized for mathematics, programming, and scientific problems. It achieves higher accuracy with a smaller parameter scale, featuring low training cost and high efficiency.

Natural Language ProcessingTransformersEnglish

PARD is a high-performance speculative decoding method that can convert autoregressive draft models into parallel draft models at low cost, significantly improving the inference speed of large language models and reducing deployment complexity and adaptation costs.

Natural Language ProcessingTransformers

PARD is a high-performance speculative decoding method that can convert autoregressive draft models into parallel draft models at low cost, significantly accelerating the inference of large language models.

Natural Language ProcessingTransformers

Thinkless is a large language model trained via reinforcement learning, capable of adaptively selecting short or long-chain reasoning modes, significantly reducing inference computational costs.

Natural Language ProcessingTransformers

A fine-tuned Mixture of Experts version based on Qwen3-30B-A3B-Base, increasing activated experts from 8 to 16 for enhanced complex task handling at the cost of reduced inference speed

Natural Language ProcessingTransformers

JetMoE-8B is an efficient open-source large language model that surpasses LLaMA2-7B performance with a low training cost of $100,000, activating only 2.2 billion parameters during inference

Natural Language ProcessingTransformers

A linear computational cost inference model based on RetNet architecture hybridized with Transformer, achieved through cross-architecture transfer learning

Natural Language ProcessingTransformersEnglish