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 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

Qwen3-32B is the latest generation of large language models in the Qwen series, with powerful reasoning, instruction-following, intelligent agent interaction, and multilingual processing capabilities. It supports over 100 languages and dialects and can seamlessly switch between thinking and non-thinking modes, providing users with a natural and smooth dialogue experience.

Natural Language ProcessingTransformers

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

A large language model with 12B parameters fine-tuned based on Qwen 3, capable of handling 256k ultra-long contexts. It is specifically optimized for long text generation tasks. This model combines the foundation of the VLTO 8B model, Brainstorm 20x tuning, and NEO Imatrix dataset quantization technology, enabling it to generate high-quality long text content.

Natural Language ProcessingGgufMultiple Languages

Prima-24B is a large language model with 24 billion parameters, which is quantized and optimized through the GGUF format and specifically optimized for role-playing and creative writing tasks. This model is converted from the original Prima-24B model, using the Q4_K_M quantization level, which significantly reduces the model size and memory requirements while maintaining good performance.

Natural Language ProcessingGgufEnglish

The top model in the aquif-3.5 series, with advanced inference capabilities and a large context window of 1 million tokens, and has excellent performance in multiple benchmark tests. The comprehensive AAII score reaches 60 points.

Natural Language ProcessingTransformersMultiple Languages

Kimi-K2-Thinking is a large language model in MLX format converted by mlx-community from the original model of moonshotai. The conversion is carried out using version 0.28.4 of mlx-lm, retaining the chain of thought reasoning ability of the original model.

Natural Language ProcessingMlx

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

Qwen3-Coder-480B-A35B-Instruct is a large code generation model with 480 billion parameters, supporting 8.5-bit quantization and optimized based on the MLX framework. This model is specifically designed for code generation tasks and can run efficiently on devices with sufficient memory.

Natural Language ProcessingMlxEnglish

Apollo-V0.1-4B-Thinking is a large language model based on 4B parameters, adopting a chain of thought reasoning architecture and specifically optimized for reasoning tasks. This model offers multiple quantization versions and can run efficiently in different hardware environments.

Natural Language ProcessingGguf

JanusCoder-8B is an open-source code intelligence foundation model built on Qwen3-8B, aiming to establish a unified visual programming interface. This model is trained on JANUSCODE-800K (the largest multimodal code corpus to date) and can handle various visual programming tasks, including data visualization, interactive web UI, and code-driven animation.

MultimodalTransformers

gpt-oss-safeguard-20b is a security inference model fine-tuned based on GPT-OSS-20b, specifically designed for input-output filtering of large language models, online content annotation, and offline annotation for trust and security use cases. This model uses the Apache 2.0 license and supports custom policies and a transparent decision-making process.

Natural Language ProcessingTransformers

This is the GGUF quantized version of NVIDIA's Qwen3-Nemotron-32B-RLBFF large language model. It uses the llama.cpp tool for multi-precision quantization, offering more than 20 quantization options from BF16 to IQ2_XXS, suitable for different hardware configurations and performance requirements.

Natural Language ProcessingGguf

This is an efficient text summarization model specifically designed to compress text content in large language model prompts, achieving a compression ratio of over 7 times and significantly reducing API call costs.

Natural Language ProcessingTransformersEnglish

Emu3.5 is a native multimodal model developed by the Beijing Academy of Artificial Intelligence (BAAI). It can jointly predict the next state across vision and language, enabling coherent world modeling and generation. After end-to-end pre-training and large-scale reinforcement learning post-training, it demonstrates excellent performance in multimodal tasks.

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

Hebrew_Nemo is a large language model focused on Hebrew processing. It has undergone instruction fine-tuning and supports text generation and dialogue interaction. This version is the GGUF quantized version of the original model, offering multiple quantization levels.

Natural Language ProcessingTransformersMultiple Languages

This is a quantized version based on the SicariusSicariiStuff/Hebrew_Nemo model, a large language model with the Mistral architecture specifically optimized for Hebrew, supporting text generation and dialogue tasks in Hebrew and English.

Natural Language ProcessingTransformersMultiple Languages

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