# INT4 Weight-only Quantization and Deployment (W4A16) LMDeploy adopts [AWQ](https://arxiv.org/abs/2306.00978) algorithm for 4bit weight-only quantization. By developed the high-performance cuda kernel, the 4bit quantized model inference achieves up to 2.4x faster than FP16. LMDeploy supports the following NVIDIA GPU for W4A16 inference: - Turing(sm75): 20 series, T4 - Ampere(sm80,sm86): 30 series, A10, A16, A30, A100 - Ada Lovelace(sm90): 40 series Before proceeding with the quantization and inference, please ensure that lmdeploy is installed. ```shell pip install lmdeploy[all] ``` This article comprises the following sections: - [Inference](#inference) - [Evaluation](#evaluation) - [Service](#service) ## Inference Trying the following codes, you can perform the batched offline inference with the quantized model: ```python from lmdeploy import pipeline, TurbomindEngineConfig engine_config = TurbomindEngineConfig(model_format='awq') pipe = pipeline("internlm/internlm2-chat-20b-4bits", backend_config=engine_config) response = pipe(["Hi, pls intro yourself", "Shanghai is"]) print(response) ``` For more information about the pipeline parameters, please refer to [here](https://github.com/InternLM/lmdeploy/blob/main/docs/en/inference/pipeline.md). ## Evaluation Please overview [this guide](https://opencompass.readthedocs.io/en/latest/advanced_guides/evaluation_turbomind.html) about model evaluation with LMDeploy. ## Service LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup: ```shell lmdeploy serve api_server internlm/internlm2-chat-20b-4bits --backend turbomind --model-format awq ``` The default port of `api_server` is `23333`. After the server is launched, you can communicate with server on terminal through `api_client`: ```shell lmdeploy serve api_client http://0.0.0.0:23333 ``` You can overview and try out `api_server` APIs online by swagger UI at `http://0.0.0.0:23333`, or you can also read the API specification from [here](https://github.com/InternLM/lmdeploy/blob/main/docs/en/serving/restful_api.md).