Calcium-Opus-14B-Merge is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. These models have proven effective in context understanding, reasoning, and mathematical problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets, with a focus on chain-of-thought (CoT) reasoning for problem-solving. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks.

This is a merge of pre-trained language models created using mergekit.

This model was merged using the Model Stock merge method using Qwen/Qwen2.5-14B-Instruct as a base.

The following models were included in the merge:

The following YAML configuration was used to produce this model:

Detailed results can be found here! Summarized results can be found here!