This model is a GGUF format conversion version of cerebras/Qwen3-Coder-REAP-25B-A3B, which uses the llama.cpp tool for format conversion and quantization processing. The original model is a large language model with 25B parameters, specifically optimized for code generation tasks.

Natural Language ProcessingTransformersEnglish

This is a quantized version of the Ling-mini-2.0 model from inclusionAI, quantized using the imatrix technology of llama.cpp. It offers various quantization options from 32.54GB BF16 to 4.66GB IQ2_XS and can run efficiently in multiple hardware environments.

Natural Language ProcessingGguf

The INL architecture is a production-level neural architecture based on integral neuron dynamics, replacing traditional feed-forward network layers with iterative dynamics. It is suitable for various types of models such as large language models, vision transformers, and multi-modal models.

Natural Language ProcessingSafetensorsEnglish

GLM-4.5-Air-REAP-82B-A12B is an efficiently compressed version of GLM-4.5-Air. Through the REAP pruning technology, the parameter scale is compressed from 106B to 82B, reducing the memory requirement by 25% while maintaining nearly lossless performance.

Natural Language ProcessingTransformersEnglish

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

Tesity-T5 is an encoder-decoder type language model developed by Clemylia, focusing on contextual Q&A tasks. This model can accurately extract and synthesize answers based on the provided text context, with the ability to provide factual and comprehensive responses.

Natural Language ProcessingTransformersFrench

DreamOmni2-GGUF is an image-to-image model that converts the xiabs/DreamOmni2-7.6B model to the GGUF format. It supports multimodal instruction editing and generation and follows the Apache-2.0 license.

MultimodalGguf

This project is a quantized version of the TECHNOPRAVIN01/Qwen2.5-14B-Valor model, providing model files in various quantization formats, including multiple quantization types such as IQ1_S, IQ2_XS, IQ3_M, Q4_K_S, Q5_K_M, etc., which are convenient for use in different scenarios.

Natural Language ProcessingTransformersEnglish

This project is a static quantized version based on the TECHNOPRAVIN01/Qwen2.5-14B-Valor model, offering multiple quantization types to meet the usage requirements under different hardware conditions. The model reduces the model size through quantization technology while maintaining good performance.

Natural Language ProcessingTransformersEnglish

This is a collection of quantized versions of the inclusionAI/Ling-1T model, developed based on the ik_llama.cpp branch. It offers multiple quantization schemes to meet different memory and performance requirements. These quantized models provide excellent perplexity performance under a given memory footprint and are specifically optimized for the inference efficiency of the MoE architecture.

Natural Language ProcessingGguf

This is a LoRA model based on Qwen-Image-Edit-2509, specifically designed to accurately apply design patterns to people's shirts, providing new possibilities for image editing.

Computer VisionDiffusers

DeepSeek-OCR is an advanced optical character recognition model developed by DeepSeek AI, focusing on exploring the boundaries of visual text compression and capable of efficiently extracting and converting text content from images.

MultimodalSafetensorsOther

Qwen3-VL-TimeTravel is a version fine-tuned on the MBZUAI/TimeTravel dataset using the Unsloth library based on the Qwen3-VL-8B-Instruct model. This model is specifically designed to generate descriptions of historical cultural relic images and has professional capabilities in historical and cultural relic analysis.

MultimodalTransformersEnglish

Qwen3-VL is the most powerful vision-language model in the Tongyi series, achieving a comprehensive upgrade in all aspects. 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 abilities.

MultimodalSafetensors

Qwen3-VL is the most powerful vision-language model in the Tongyi large model series, with comprehensive upgrades in text understanding, visual perception, spatial understanding, video processing, etc., providing excellent multimodal capabilities.

MultimodalSafetensors

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 agent interaction capabilities.

MultimodalSafetensors

This is the 8-bit quantized version of the Qwen3-VL-4B-Instruct model in MLX format, converted by mlx-community. This model is a multimodal vision-language model with 4 billion parameters, supporting image understanding and text generation tasks, and optimized for instruction following scenarios.

MultimodalMlx

This is a visual language model in MLX format converted from the Qwen3-VL-30B-A3B-Thinking model, supporting image understanding and multimodal dialogue, and optimized with bf16 precision.

MultimodalMlx

Qwen-1.5B_THIP is a mathematical reasoning model fine-tuned using the GRPO method on the DeepMath-103k mathematical dataset based on DeepSeek-R1-Distill-Qwen-1.5B with the TRL framework. This model is specifically optimized for solving mathematical problems and has strong mathematical reasoning abilities.

Natural Language ProcessingTransformers

Qwen3-VL-8B-Thinking is an 8-billion-parameter multimodal large language model developed by the Qwen team at Alibaba Cloud. It is designed for deep multimodal reasoning, supporting visual understanding, long context processing, and structured chain-of-thought generation, and performs excellently in complex reasoning tasks.

MultimodalMlx