Delta-CoMe: A Novel Incremental Compression Algorithm Revolutionizing Large Language Model Deployment
Introduction: The burgeoning field of large language models (LLMs)faces a significant hurdle: their immense size and resource demands. Deploying multiple LLMs, particularly on resource-constrained hardware, has been a major challenge.However, a groundbreaking new algorithm, Delta-CoMe, developed through a collaborative effort between Tsinghua University’s NLP lab, the OpenBMB open-source community, Peking University, and Shanghai University of Finance and Economics, offers a potential solution. This innovative incremental compression algorithm allows for the efficient deployment of multiple 7B parameter models on a single 80GB A100GPU, a feat previously unimaginable.
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Delta-CoMe leverages a novel approach combining low-rank decomposition and low-bit quantization techniques. Instead of compressing the entire model, it focuses on the incremental changes (Delta) in model parameters between different versions or fine-tuned models. By exploiting the low-rank characteristics of these Delta parameters, Delta-CoMe achieves mixed-precision compression. This clever strategy dramatically reduces both storage and inference costs while maintaining near-lossless performance.
This is particularly significantfor complex tasks. The researchers found that Delta-CoMe excels in handling mathematical problems, code generation, and multi-modal tasks – areas where traditional compression methods often suffer performance degradation. The algorithm’s effectiveness is highlighted by its ability to load up to 50 seven-billion parameter models on a single80GB A100 GPU, representing an approximately eightfold reduction in memory footprint compared to deploying the models individually.
Key Features of Delta-CoMe:
- Model Compression: Utilizes mixed-precision compression to significantly reduce the storage and memory requirements of LLMs. This enables thedeployment of a substantially larger number of models on limited hardware.
- Performance Preservation: Maintains the performance of the original models, particularly crucial for complex tasks such as mathematical problem-solving, code generation, and multi-modal applications. The compressed models achieve performance nearly identical to their uncompressed, fine-tuned counterparts.
- Multi-task Handling: Supports the simultaneous deployment of multiple models with diverse capabilities, ideal for multi-tenant and multi-task scenarios. This enhances the flexibility and efficiency of model deployment.
- Inference Speed Improvement: The algorithm is optimized for Triton kernels, further accelerating the inferenceprocess.
Conclusion:
Delta-CoMe represents a significant advancement in LLM deployment. By efficiently compressing the incremental changes in model parameters, it overcomes the limitations imposed by the massive size of LLMs. This breakthrough allows researchers and developers to deploy a significantly larger number of models on existing hardware, openingup new possibilities for multi-model applications and research. Future research could explore the application of Delta-CoMe to even larger models and investigate further optimizations for specific hardware architectures. The open-source nature of Delta-CoMe ensures its accessibility to the wider research community, fostering further innovation and development in the fieldof LLM compression.
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