Introduction to LMDeploy
LMDeploy is a cutting-edge toolkit designed for the compression, deployment, and serving of large language models (LLMs). It is a collaborative effort spearheaded by the MMRazor and MMDeploy teams. The toolkit is renowned for its efficient and innovative solutions in the realm of LLMs, offering several core features that enhance its usability and performance.
Core Features
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Efficient Inference: LMDeploy achieves exceptional performance by incorporating advanced techniques like persistent batching, blocked KV cache, dynamic split and fuse operations, and tensor parallelism. These innovations lead to an impressive 1.8x increase in request throughput compared to comparable tools.
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Effective Quantization: The toolkit supports both weight-only and key/value (k/v) quantization. Its 4-bit inference performance is reportedly 2.4 times faster than the traditional FP16 approach. Notably, the quality of this quantization has been validated through rigorous evaluation by OpenCompass.
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Effortless Distribution Server: LMDeploy simplifies the deployment of multi-model services, allowing for seamless operation across multiple machines and devices using its intuitive request distribution service.
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Interactive Inference Mode: It has a unique capability to cache the k/v of attention during multi-round dialogue processes. This feature allows the system to remember historical conversations, thereby preventing redundant processing of past dialogue.
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Excellent Compatibility: LMDeploy supports various advanced features such as KV Cache Quant, AWQ, and Automatic Prefix Caching, which can be utilized in conjunction to enhance overall system performance.
Performance
LMDeploy is engineered to provide high-performance inference benchmarks across a variety of devices. Detailed performance metrics are available for devices such as A100, V100, as well as several others.
Supported Models
LMDeploy supports a comprehensive range of models, including but not limited to:
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LLMs (Large Language Models): Versions of Llama, InternLM, Qwen, Baichuan, Code Llama, and more, with model sizes ranging broadly from smaller millions to larger billion parameters.
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VLMs (Vision Language Models): Models such as LLaVA, InternLM-XComposer, Qwen-VL, DeepSeek-VL, etc.
These models are supported by two distinct inference engines provided by LMDeploy: TurboMind and PyTorch. TurboMind is optimized for maximal inference performance, whereas the PyTorch engine is Python-based, thus more accessible for developers looking to experiment and develop new features.
Quick Start Guide
To begin using LMDeploy, it is recommended to install it via pip within a conda environment. This process ensures compatibility and ease of setup.
conda create -n lmdeploy python=3.8 -y
conda activate lmdeploy
pip install lmdeploy
For offline batch inference, users can leverage a simple Python script to get started with model deployment.
Tutorials and Contributing
LMDeploy offers a plethora of tutorials for both beginners and advanced users, ensuring everyone from novices to experienced developers can make full use of its capabilities. Additionally, the project welcomes contributions from the community to continue its development and improve upon its functionalities.
Acknowledgments
LMDeploy acknowledges the contributions and influence of various third-party projects like NVIDIA's FasterTransformer and Microsoft's DeepSpeed-MII, which have been instrumental in shaping its development.
Conclusion
LMDeploy positions itself as a versatile and powerful toolkit for deploying large language models efficiently and effectively. Its robust feature set, coupled with strong community and developer support, makes it a significant tool for anyone working in the realm of artificial intelligence and machine learning.