Aquif-3.5-Max-42B-A3B is a large language model with 42 billion parameters, which has undergone MXFP4_MOE quantization processing. It optimizes inference efficiency while maintaining high-quality text generation capabilities. This model is based on an advanced mixture-of-experts architecture and is suitable for various natural language processing tasks.

Natural Language ProcessingGguf

This is the MXFP4_MOE quantized version of the Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL model, specifically optimized for text generation tasks. The model is based on a large language model architecture with 42B parameters. Through quantization technology, it reduces the computational and storage requirements while maintaining good performance.

Natural Language ProcessingGguf

Qwen3-42B-A3B-2507 is a large language model with 42B parameters based on the Qwen3 architecture. It performs excellently in code generation and text generation tasks, supports multilingual processing, and has enhanced reasoning ability. This version has been specially optimized and shows performance improvements compared to previous versions in multiple benchmark tests.

Natural Language ProcessingMlxMultiple Languages

GLM-4.5V-AWQ-4bit is a quantized multimodal model built on Zhipu AI's next-generation flagship text foundation model. Optimized by AWQ-4bit quantization technology, it significantly reduces the computational resource requirements while maintaining excellent performance. This model achieves SOTA performance among models of the same scale in 42 public visual language benchmark tests and has powerful visual reasoning capabilities.

MultimodalTransformersMultiple Languages

A 42B parameter MOE architecture model upgraded from the Qwen3-30B-A3B model, which enhances creative writing and programming abilities through Brainstorm 20x technology

Natural Language ProcessingTransformersMultiple Languages

A distilled version of the genmoai mochi-1 model transformer, composed of 42 modules (original version has 48 modules), achieving lightweight through iterative removal of modules with the smallest MSE values

MultimodalDiffusersEnglish

A large Bengali language model optimized based on the Llama-3.2-3B architecture, with 42K Bengali tokens extended and fine-tuned, performing excellently in Bengali understanding and generation tasks.

Natural Language ProcessingTransformersOther

SambaLingo-Turkish-Base is a bilingual (Turkish and English) model based on Llama-2-7b pre-training, adapted for Turkish by training on 42 billion tokens from the Turkish portion of the Cultura-X dataset.

Natural Language ProcessingTransformersMultiple Languages

An efficient text-to-image generation model based on Würstchen architecture, achieving fast inference and low-cost training through a 42x compression factor

MultimodalDiffusers

This is a 42M-parameter Llama 2 architecture float32 precision model trained on the TinyStories dataset, suitable for simple text generation tasks.

Natural Language ProcessingTransformers

A 1.3B parameter instruction-following large language model developed by 42dot, based on the supervised fine-tuned version of LLaMA 2 architecture

Natural Language ProcessingTransformersMultiple Languages

42dot LLM-PLM is a pre-trained language model developed by 42dot, supporting Korean and English text generation tasks.

Natural Language ProcessingTransformersMultiple Languages

Würstchen is an efficient text-to-image diffusion model that significantly reduces computational costs through 42x spatial compression technology

MultimodalDiffusers

An English-to-Korean translation model fine-tuned from KETI-AIR/long-ke-t5-base, trained on multiple AI Hub datasets with a BLEU score of 42.463.

Natural Language ProcessingTransformersMultiple Languages

YOLOS is a vision Transformer (ViT)-based object detection model trained with DETR loss, achieving 42 AP performance on the COCO dataset.

Computer VisionTransformers

Funnel-transformer base model trained on 70GB Korean text dataset, using 42,000 lowercase subword units

Natural Language ProcessingTransformersKorean

QARiB is a BERT model based on Arabic and its dialects, trained on 420 million tweets and 180 million text sentences, suitable for various NLP tasks.

Natural Language ProcessingJaxArabic

Albert base model trained on 70GB Korean text dataset, using 42,000 lowercase subword units

Natural Language ProcessingTransformersKorean

This is a GPT-2 base model specifically optimized for the Korean language. It is trained using a 70GB Korean text dataset and employs a tokenizer with 42,000 lowercase subwords, enabling better handling of the language characteristics of Korean.

Natural Language ProcessingTransformersKorean

QARiB is a large-scale pre-trained BERT model for Arabic and its dialects, trained on 420 million tweets and 180 million text sentences.

Natural Language ProcessingJaxArabic