# Multi-domain Q&A

Enhanced reasoning model based on Qwen-2.5 14B architecture, optimized for general reasoning and Q&A scenarios, supporting 128K context and 8K output

Theta-Lyrae-Qwen-14B is a 14-billion-parameter model based on the Qwen 2.5 14B modal architecture, optimized for general reasoning and Q&A capabilities, excelling in context understanding, logical reasoning, and multi-step problem-solving.

FP4 quantized version of DeepSeek R1 model, using optimized Transformer architecture for efficient text generation

SILMA Kashif 2B Instruct v1.0 is an open-source model designed specifically for Arabic and English RAG (Retrieval Augmented Generation) tasks. It is built on Google Gemma and has the capabilities of entity extraction and multi-domain processing.

A large Korean language model fine-tuned based on LlaMA3.1, adapted to Korean culture, and supporting Korean tasks in 53 domains

Llama-3.1-Storm-8B is a model developed based on Llama-3.1-8B-Instruct, aiming to improve the dialogue and function call capabilities of models with 8 billion parameters.

A dialogue model trained on diverse datasets, supporting Chinese interaction, suitable for non-commercial scenarios

This is a Polish text ranking model trained using Mean Squared Error (MSE) distillation method, with a training dataset containing 1.4 million queries and 10 million document text pairs.

This is a Polish text ranking model trained using the RankNet loss function, with a training dataset consisting of 1.4 million queries and 10 million document pairs.

This is a 160M-parameter Llama chat model, fine-tuned from JackFram/llama-160m, focusing on text generation tasks.