69 lines
2.4 KiB
Markdown
69 lines
2.4 KiB
Markdown
---
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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_5-7b-chat-4bit", 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_5-7b-chat-4bit --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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