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--- |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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--- |
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<div align="center"> |
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<img src="https://raw.githubusercontent.com/InternLM/lmdeploy/0be9e7ab6fe9a066cfb0a09d0e0c8d2e28435e58/resources/lmdeploy-logo.svg" width="450"/> |
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</div> |
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# INT4 Weight-only Quantization and Deployment (W4A16) |
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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. |
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LMDeploy supports the following NVIDIA GPU for W4A16 inference: |
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- Turing(sm75): 20 series, T4 |
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- Ampere(sm80,sm86): 30 series, A10, A16, A30, A100 |
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- Ada Lovelace(sm90): 40 series |
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Before proceeding with the quantization and inference, please ensure that lmdeploy is installed. |
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```shell |
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pip install lmdeploy[all] |
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``` |
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This article comprises the following sections: |
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<!-- toc --> |
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- [Inference](#inference) |
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- [Evaluation](#evaluation) |
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- [Service](#service) |
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<!-- tocstop --> |
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## Inference |
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Trying the following codes, you can perform the batched offline inference with the quantized model: |
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```python |
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from lmdeploy import pipeline, TurbomindEngineConfig |
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engine_config = TurbomindEngineConfig(model_format='awq') |
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pipe = pipeline("internlm/internlm2-chat-20b-4bits", backend_config=engine_config) |
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response = pipe(["Hi, pls intro yourself", "Shanghai is"]) |
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print(response) |
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``` |
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For more information about the pipeline parameters, please refer to [here](https://github.com/InternLM/lmdeploy/blob/main/docs/en/inference/pipeline.md). |
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## Evaluation |
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Please overview [this guide](https://opencompass.readthedocs.io/en/latest/advanced_guides/evaluation_turbomind.html) about model evaluation with LMDeploy. |
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## Service |
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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: |
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```shell |
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lmdeploy serve api_server internlm/internlm2-chat-20b-4bits --backend turbomind --model-format awq |
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``` |
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The default port of `api_server` is `23333`. After the server is launched, you can communicate with server on terminal through `api_client`: |
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```shell |
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lmdeploy serve api_client http://0.0.0.0:23333 |
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``` |
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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). |
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