Introduction to LMDeploy
LMDeploy is a powerful toolkit designed for compressing, deploying, and serving Large Language Models (LLMs). Developed by the MMRazor and MMDeploy teams, it offers a range of features that enhance the performance and efficiency of LLMs in various applications.
Key Features:
- Efficient Inference: Achieves up to 1.8x higher request throughput compared to vLLM through advanced techniques like persistent batching and high-performance CUDA kernels.
- Effective Quantization: Supports weight-only and k/v quantization, with 4-bit inference performance being 2.4x higher than FP16, validated by OpenCompass evaluation.
- Effortless Distribution Server: Simplifies the deployment of multi-model services across multiple machines and GPUs.
- Interactive Inference Mode: Remembers dialogue history during multi-round interactions, reducing redundant processing.
- Excellent Compatibility: Supports simultaneous use of KV Cache Quant, AWQ, and Automatic Prefix Caching.
Benefits:
- Optimized Performance: Tailored for high throughput and low latency in LLM applications.
- User-Friendly: Easy installation and setup, with comprehensive documentation and tutorials available.
- Community Driven: Open-source contributions are encouraged, fostering a collaborative development environment.
Highlights:
- Two inference engines: TurboMind for performance optimization and PyTorch for ease of use.
- Supports a wide range of models including Llama, InternLM, and Qwen series.
- Regular updates and enhancements to support new models and features.
