v0.6.0

• Introduced general multi-dimensional heterogeneous parallelism and CPU-based communication between different chips.
• Added comprehensive support for data processing and faster distributed training of LLaVA-OneVision, achieving SOTA results on the Infinity-MM dataset.
• Open-sourced the optimized CFG implementation and accelerated the generation and understanding tasks for Emu3.
• Implemented the auto-tuning feature to simplify large-scale distributed training, making it more accessible for users with less expertise.
• Enhanced the CI/CD system to facilitate more efficient unit testing across different backends and perform the loss check for the various parallel strategies.