This is a quantized version of the Huihui Qwen3-VL-8B thinking ablated model. Based on the Qwen3-VL-8B architecture, it is specifically optimized and quantized for image-text conversion tasks and can be efficiently run through the llama.cpp tool.

MultimodalGguf

The quantized version of the Huihui Qwen3-VL-4B thinking elimination model, optimized based on the Qwen3-VL-4B model, is specifically designed to eliminate the thinking chain reasoning process and can be used with the latest llama.cpp to provide efficient image-text to text processing capabilities.

MultimodalGguf

Qwen3-VL is the most powerful vision-language model in the Qwen 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 agent interaction capabilities. This version is a thinking-enhanced version with 2B parameters, specifically optimized for reasoning capabilities.

MultimodalTransformers

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

Qwen3-VL-32B-Thinking is the most powerful vision-language model in the Qwen series. It has excellent text understanding and generation capabilities, in-depth visual perception and reasoning abilities, long context support, strong spatial and video dynamic understanding abilities, and outstanding agent interaction capabilities. This version adopts a thinking architecture for enhanced reasoning, supporting dense architectures from edge to cloud and mixture-of-experts model architectures.

MultimodalTransformers

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

Qwen3-VL is the most powerful vision-language model in the Tongyi series. It has excellent text understanding and generation capabilities, in-depth visual perception and reasoning capabilities, long context support, enhanced spatial and video understanding capabilities, and strong agent interaction capabilities. This model is the thinking version with 2B parameters, specifically enhanced for reasoning capabilities.

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

Looking_Glass-llama is an intermediate model for testing, based on the llama 3 architecture and specifically trained for thinking tasks. This model is planned for merging and continuous training. Currently, there are certain repetition issues. It is recommended to use a higher temperature value and repetition penalty coefficient.

Natural Language ProcessingTransformers

GA Guard Thinking is an open-source moderation model with 4 billion parameters developed by General Analysis. It is specifically designed to detect and prevent language models from generating unsafe and non-compliant content. The model is trained on seven security categories and can effectively identify risky content such as illegal activities, hate speech, and personal information leakage.

Natural Language ProcessingTransformersEnglish

This is a mixture-of-experts model based on Qwen3-Coder-30B-A3B-Instruct, with 54 billion parameters and a context length of 1 million. The model has powerful programming capabilities and general scenario processing capabilities through three-step merging and Brainstorm 40X optimization. It is especially integrated with a thinking module, which can perform in-depth reasoning before answering.

Natural Language ProcessingTransformersMultiple Languages

Qwen3-VL is the most powerful vision-language model in the Qwen 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 version uses a mixture-of-experts model architecture and supports enhanced reasoning thinking functions.

MultimodalSafetensors

The palmyra-mini series of models demonstrates excellent capabilities in the fields of complex reasoning and mathematical problem-solving, especially performing outstandingly in benchmark tests that require in-depth understanding and multi-step thinking. This model is proficient in solving elementary school-level math problems, achieving a high score of 0.818 in the gsm8k benchmark test, also obtaining a score of 0.818 in the MATH500 benchmark test, and a stable score of 0.6 in the AMC23 benchmark test.

Natural Language ProcessingTransformersMultiple Languages

NVIDIA Nemotron Nano 9B v2 is a high-performance large language model developed by NVIDIA. It adopts the Mamba2-Transformer hybrid architecture, supports multilingual inference and chat tasks, and performs excellently in multiple benchmark tests. It especially supports the runtime 'thinking' budget control function.

Natural Language ProcessingTransformersMultiple Languages

MiniCPM4.1-8B-GGUF is a GGUF quantized format version of the MiniCPM4.1-8B model. It is an efficient large language model designed for edge devices, with 8 billion parameters, supporting integrated thinking, and can achieve more than 5 times the generation acceleration on typical edge chips.

Natural Language ProcessingTransformersMultiple Languages

Hermes 4 14B is a cutting-edge hybrid mode reasoning model developed by Nous Research based on Qwen 3 14B. It performs excellently in mathematics, code, STEM, logic, creativity, and format-faithful output, with explicit thinking processes and tool call capabilities.

Natural Language ProcessingTransformersEnglish

DeepSeek-V3.1 is a large language model that is post-trained based on DeepSeek-V3.1-Base. It has a 128K context length and supports mixed thinking modes, intelligent tool invocation, and code proxy functions. This model performs excellently in multiple benchmark tests, especially showing significant improvements in mathematical reasoning, code generation, and search agent tasks.

Natural Language ProcessingTransformersMultiple Languages

DeepSeek-V3.1 is a large language model developed by DeepSeek-AI and is an upgraded version of DeepSeek-V3. This model supports both hybrid thinking mode and non-thinking mode, and performs excellently in tool invocation, code generation, mathematical reasoning, etc., supporting a context length of 128K.

Natural Language ProcessingTransformers

Seed-OSS-36B-Instruct-AWQ is a quantized version of the 36B parameter large language model developed by the ByteDance Seed team. It has powerful long context processing capabilities, reasoning capabilities, and agent functions, and supports a context length of up to 512K and flexible thinking budget control.

Natural Language ProcessingTransformersMultiple Languages

DeepSeek-V3.1 is a large-scale language model developed by the DeepSeek team. It supports both thinking mode and non-thinking mode and performs excellently in multiple benchmark tests. It has powerful text understanding, generation, and reasoning capabilities.

Natural Language ProcessingTransformers