Recently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities. Leading this evolution are proprietary LLMs like GPT-4 and GPT-o1, which have captured widespread attention in the AI community for their power and versatility. Simultaneously, open-source LLMs, such as LLaMA and Mistral, have made great contributions to the ever-increasing popularity of LLMs due to the ease to customize and deploy the models across various applications. Although LLMs offer unprecedented opportunities for research and innovation, its commercialization has raised concerns about transparency, reproducibility, and safety. Many open LLM models lack the necessary components (such as training code and data) for full understanding and reproducibility, and some use restrictive licenses whilst claiming to be “open-source”, which may hinder further innovations on LLMs. To mitigate this issue, we follow the Model Openness Framework (MOF), a ranked classification system that rates machine learning models based on their completeness and openness, following principles of open science, open source, open data, and open access. We present a truly open source LLM Moxin 7B and release pre-training code and configurations, training and fine-tuning data, and intermediate and final checkpoints, aiming to make continuous commitments to fully open-source LLMs. We also finetune the Moxin Base model with SOTA post-training framework and instruction data to obtain Moxin Instruct model. To improve the reasoning capability, we further finetune our model with chain-of-thought data distilled from DeepSeek R1, and then use Group Relative Policy Optimization, an efficient and effective reinforcement learning algorithm following DeepSeek R1, to finetune our model, leading to the Moxin Reasoning model.
