H2 is a framework designed to enhance the training of large language models (LLMs) on hyper-heterogeneous clusters with over 1,000 chips, addressing inefficiencies caused by diverse hardware and software environments. It integrates DiTorch for consistent programming across chips and DiComm for optimized communication, alongside an adaptive pipeline parallelism strategy that achieves significant speedup compared to traditional homogeneous training methods. Experimental results show a performance improvement of up to 16.37% on a 100-billion-parameter LLM, demonstrating the framework's effectiveness at large scales.