Qwen3-Next-80B-A3B-Instruct is an efficient sparse mixture of experts model developed by the Tongyi Qianwen team. It has a total of 80 billion parameters, with only 3 billion active parameters. The model adopts an innovative hybrid attention mechanism and a MoE architecture with an extremely low activation rate, significantly improving inference efficiency while maintaining strong performance. It natively supports a context length of 262,000 tokens and can be extended to 1 million tokens.

Natural Language ProcessingTransformers

MiniCPM4.1-8B GGUF is an efficient large language model designed specifically for edge devices. It has carried out systematic innovations in four key dimensions: model architecture, training data, training algorithm, and inference system, achieving extreme efficiency improvement. It supports a context length of 65,536 tokens and a hybrid inference mode.

Natural Language ProcessingTransformersMultiple Languages

This is a quantized version of the NVIDIA Nemotron-H-47B-Reasoning-128K model, optimized using the imatrix technology of llama.cpp. The model supports a context length of 128K and is designed for reasoning tasks. It offers a variety of quantization options from BF16 to extremely low bitrates, suitable for different hardware configurations and performance requirements.

Natural Language ProcessingGguf

A horror-optimized version based on Google's Gemma-3 model, featuring extreme quantization technology and horror enhancement matrix, supporting a 32k context window

Natural Language ProcessingGguf

LGAI's EXONE series reasoning model, featuring new matrix and extreme quantization technologies, equipped with a 32k context window, focusing on deep thinking and reasoning tasks.

Natural Language ProcessingGgufMultiple Languages

A horror-style instruction-tuned version based on Google's Gemma-3 model, featuring Neo Imatrix technology and extreme quantization, supporting 128k context length

Natural Language ProcessingGguf

An extreme quantization version based on Google's Gemma-3 model, enhanced with NEO Imatrix technology, supporting 128k context length and suitable for full-scenario tasks

Natural Language ProcessingGguf

This is the brand - new Mistral - Small - Instruct 2409 22B model, paired with the NEO IMATRIX dataset. The maximum context length can reach 131,000 (128k). This model has excellent performance, reaching 4.8611 +/- 0.06701 (Q4_K_M) at the PPL level, and it is an extremely outstanding compressed model.

Natural Language ProcessingGgufMultiple Languages