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---
license: apache-2.0
pipeline_tag: text-generation
---
<div align="center">
<img src="https://raw.githubusercontent.com/InternLM/lmdeploy/0be9e7ab6fe9a066cfb0a09d0e0c8d2e28435e58/resources/lmdeploy-logo.svg" width="450"/>
</div>
# 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:
<!-- toc -->
- [Inference](#inference)
- [Evaluation](#evaluation)
- [Service](#service)
<!-- tocstop -->
## 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).