# Long-text reasoning

This model is a GGUF format conversion based on PocketDoc/Dans-PersonalityEngine-V1.3.0-12b, supporting multiple languages and tasks, suitable for general purposes, role-playing, story creation, and other scenarios.

INT4 quantized version of Qwen3-30B-A3B, reducing disk and GPU memory requirements by 75% while maintaining high performance.

This model is converted from google/gemma-3-12b-it to GGUF format, suitable for the llama.cpp framework.

Serpens-Opus-14B-Exp is a 14-billion-parameter model based on the Qwen 2.5 14B architecture, designed to enhance reasoning capabilities for general-purpose reasoning and Q&A tasks.

A natural language inference model optimized through multi-task fine-tuning based on the ModernBERT-large model, excelling in zero-shot classification and NLI tasks.

A compact zero-shot classification model based on DeBERTa architecture, optimized for long-text natural language inference tasks, converted to ONNX format for web compatibility

Based on the DeBERTa-v3-base model, the context length is extended to 1280, and fine-tuned for 250,000 steps on the tasksource dataset, focusing on natural language inference and zero-shot classification tasks.

Based on the DeBERTa-v3-small model, extended context length to 1680 tokens, fine-tuned on the tasksource dataset, suitable for long-text natural language inference tasks