Qwen3-4B-Hivemind-Instruct-NEO-MAX-Imatrix-GGUF is a large language model with 4B parameters for instruction fine-tuning based on the Qwen3 architecture. It uses NEO Imatrix and MAX quantization technologies and has the ability to process an ultra-long context of 256k. This model performs excellently in multiple benchmark tests and is a powerful general-purpose model.

Natural Language ProcessingGgufEnglish

This model is a quantized (Int8 / Q8) version of mixedbread-ai/mxbai-edge-colbert-v0-17m, exported specifically for use with transformers.js. It includes a custom projection layer required for the 48-dimensional ColBERT architecture, aiming to provide efficient vector retrieval capabilities.

Natural Language ProcessingTransformers

This model is an MLX format conversion version of Kimi-Linear-48B-A3B-Instruct, optimized for Apple Silicon devices such as the Apple Mac Studio. It is a large language model with 48 billion parameters, supporting instruction following and suitable for local inference and conversation tasks.

Natural Language ProcessingMlx

Qwen3 VL 4B Thinking is a multimodal model that supports image-to-text and text-to-text conversion. It has a parameter scale of 4B and can meet various image-text interaction needs.

MultimodalGguf

This is a version of the Qwen3-Next-80B large language model after 4-bit quantization using Huawei's SINQ (Sinkhorn Normalized Quantization) method. SINQ is a novel, fast, and high-quality quantization method designed to significantly compress the model size (by approximately 75%) while maintaining almost the same accuracy as the original model, making it easier to deploy.

Natural Language ProcessingSafetensorsEnglish

This repository contains a 4-step distilled model optimized for HunyuanVideo-1.5, which can achieve ultra-fast 4-step inference without using CFG (Classifier-Free Guidance), significantly reducing the generation time while maintaining high-quality video output.

MultimodalDiffusers

Qwen3-VL-4B-Instruct is a 4-billion-parameter visual language model launched by Alibaba. It is developed based on the Qwen3 architecture and supports multimodal understanding and dialogue tasks. This model has powerful image understanding and text generation capabilities, and can handle complex visual language interaction scenarios.

MultimodalTransformers

This project provides a quantized GGUF version of Tencent's Hunyuan Video 1.5 text-to-video model, which supports 480P video generation tasks. It includes both distilled and full models and can be used in conjunction with tools such as ComfyUI-GGUF.

MultimodalGguf

The quantized GGUF version of Tencent's Hunyuan Video 1.5 model, specifically designed for image-to-video and video generation tasks. It offers distilled and standard models with a 480P resolution and supports multiple quantization precisions, including Q4_K_S, Q8_0, and FP16, etc.

MultimodalGguf

Qwen-SEA-LION-v4-4B-VL is a vision-language model with 4 billion parameters built on the Qwen3-VL-4B-Instruct architecture. It has been specifically instruction fine-tuned for the Southeast Asian region, possessing multilingual and multicultural capabilities, supporting English and seven Southeast Asian languages, and retaining strong vision-language understanding capabilities.

MultimodalTransformersMultiple Languages

UltraFlux is a diffusion transformer based on Flux, specifically designed for native 4K text-to-image generation. Through the collaborative design of data, architecture, and loss, it can maintain consistent image quality under various aspect ratios.

Computer VisionDiffusersEnglish

This project provides the GGUF format file of the Qwen3-4B-Thinking-2507 model. It is a large language model with 4B parameters and chain of thought reasoning ability, supporting multiple quantization versions for easy deployment and operation on various hardware.

Natural Language ProcessingGguf

VibeThinker-1.5B is a 1.5-billion-parameter dense language model launched by Weibo AI. It is fine-tuned based on Qwen2.5-Math-1.5B and is specifically designed for mathematical and algorithmic coding problems. Trained using the 'Spectrum to Signal Principle' framework, it outperforms larger models in multiple math competition tests. The training cost is approximately $7,800, and it supports an output of up to about 40k tokens.

Natural Language ProcessingTransformersMultiple Languages

DeepMath is a mathematical reasoning model with 4 billion parameters, built on Qwen3 - 4B Thinking, combining a fine - tuned large language model and a sandboxed Python executor. It can generate concise Python code snippets for calculation steps, significantly reducing errors and shortening the output length.

Natural Language ProcessingTransformersEnglish

Nanbeige 4-3B-Thinking-2511 is the latest enhanced version of the Nanbeige series. Through advanced distillation technology and reinforcement learning optimization, it achieves powerful reasoning ability within a compact 3B parameter scale. This model has achieved state-of-the-art (SOTA) results among models with less than 32B parameters in benchmark tests such as Arena-Hard-V2 and BFCL-V4.

Natural Language ProcessingTransformersMultiple Languages

The 4-bit quantized version of VibeThinker-1.5B, optimized for Apple chips based on the MLX framework, is a dense language model with 1.5 billion parameters, specifically designed for mathematical reasoning and algorithm coding problems.

Natural Language ProcessingMlxMultiple Languages

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 is a quantized version of the p-e-w/gpt-oss-20b-heretic model, quantized using the imatrix technology of llama.cpp. The model is a large language model with 20 billion parameters, offering a variety of quantization options ranging from high quality to low quality, with file sizes from 41.86GB to 11.52GB, suitable for different hardware conditions.

Natural Language ProcessingGguf

Wan2.2-TI2V-5B-Turbo-GGUF is an image-to-video model converted from the quanhaol/Wan2.2-TI2V-5B-Turbo base model. It has been optimized to run on a 4GB GPU and has excellent versatility and efficient inference capabilities.

MultimodalGguf

This is a large language model with 30 billion parameters trained on the Claude Sonnet 4.5 reasoning dataset. It is specifically optimized for high-difficulty reasoning tasks and performs excellently in the fields of coding and science.

Natural Language ProcessingTransformersEnglish