Package: lmdeploy
English |简体中文👋 join us onTwitter,DiscordandWeChatLatest News 🎉2024[2024/01]OpenAOEseamless integration withLMDeploy Serving Service.[2024/01] Support for multi-model, multi-machine, multi-card inference services. For usage instructions, please refer tohere[2024/01] SupportPyTorch inference engine, developed entirely in Python, helping to lower the barriers for developers and enable  rapid experimentation with new features and technologies.2023[2023/12] Turbomind supports multimodal input.Gradio Demo[2023/11] Turbomind supports loading hf model directly. Clickherefor details.[2023/11] TurboMind major upgrades, including: Paged Attention, faster attention kernels without sequence length limitation, 2x faster KV8 kernels, Split-K decoding (Flash Decoding), and W4A16 inference for sm_75[2023/09] TurboMind supports Qwen-14B[2023/09] TurboMind supports InternLM-20B[2023/09] TurboMind supports all features of Code Llama: code completion, infilling, chat / instruct, and python specialist. Clickherefor deployment guide[2023/09] TurboMind supports Baichuan2-7B[2023/08] TurboMind supports flash-attention2.[2023/08] TurboMind supports Qwen-7B, dynamic NTK-RoPE scaling and dynamic logN scaling[2023/08] TurboMind supports Windows (tp=1)[2023/08] TurboMind supports 4-bit inference, 2.4x faster than FP16, the fastest open-source implementation. Checkthisguide for detailed info[2023/08] LMDeploy has launched on theHuggingFace Hub, providing ready-to-use 4-bit models.[2023/08] LMDeploy supports 4-bit quantization using theAWQalgorithm.[2023/07] TurboMind supports Llama-2 70B with GQA.[2023/07] TurboMind supports Llama-2 7B/13B.[2023/07] TurboMind supports tensor-parallel inference of InternLM.IntroductionLMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by theMMRazorandMMDeployteams. It has the following core features:Efficient Inference: LMDeploy delivers up to 1.8x higher request throughput than vLLM, by introducing key features like persistent batch(a.k.a. continuous batching), blocked KV cache, dynamic split&fuse, tensor parallelism, high-performance CUDA kernels and so on.Effective Quantization: LMDeploy supports weight-only and k/v quantization, and the 4-bit inference performance is 2.4x higher than FP16. The quantization quality has been confirmed via OpenCompass evaluation.Effortless Distribution Server: Leveraging the request distribution service, LMDeploy facilitates an easy and efficient deployment of multi-model services across multiple machines and cards.Interactive Inference Mode: By caching the k/v of attention during multi-round dialogue processes, the engine remembers dialogue history, thus avoiding repetitive processing of historical sessions.PerformanceFor detailed inference benchmarks in more devices and more settings, please refer to the following link:A100V100409030902080Supported ModelsModelSizeLlama7B - 65BLlama27B - 70BInternLM7B - 20BInternLM27B - 20BInternLM-XComposer7BQWen7B - 72BQWen-VL7BBaichuan7B - 13BBaichuan27B - 13BCode Llama7B - 34BChatGLM26BFalcon7B - 180BYI6B - 34BLMDeploy has developed two inference engines -TurboMindandPyTorch, each with a different focus. The former strives for ultimate optimization of inference performance, while the latter, developed purely in Python, aims to decrease the barriers for developers.They differ in the types of supported models and the inference data type. Please refer tothis tablefor each engine's capability and choose the proper one that best fits your actual needs.Quick StartInstallationInstall lmdeploy with pip ( python 3.8+) orfrom sourcepipinstalllmdeployThe default prebuilt package is compiled on CUDA 11.8. However, if CUDA 12+ is required, you can install lmdeploy by:exportLMDEPLOY_VERSION=0.2.0exportPYTHON_VERSION=38pipinstallhttps://github.com/InternLM/lmdeploy/releases/download/v${LMDEPLOY_VERSION}/lmdeploy-${LMDEPLOY_VERSION}-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux2014_x86_64.whlOffline Batch Inferenceimportlmdeploypipe=lmdeploy.pipeline("internlm/internlm-chat-7b")response=pipe(["Hi, pls intro yourself","Shanghai is"])print(response)[!NOTE]
By default, LMDeploy downloads model from HuggingFace. If you would like to use models from ModelScope, please install ModelScope bypip install modelscopeand set the environment variable:export LMDEPLOY_USE_MODELSCOPE=TrueFor more information about inference pipeline, please refer tohere.TutorialsPlease overviewgetting_startedsection for the basic usage of LMDeploy.For detailed user guides and advanced guides, please refer to ourtutorials:User GuideInference pipelineInference Engine - TurboMindInference Engine - PyTorchServingQuantizationAdvance GuideAdd chat templateAdd a new modelgemm tuningLong context inferenceMulti-model inference serviceContributingWe appreciate all contributions to LMDeploy. Please refer toCONTRIBUTING.mdfor the contributing guideline.AcknowledgementFasterTransformerllm-awqvLLMDeepSpeed-MIILicenseThis project is released under theApache 2.0 license.