README.md

---
license: mit
---
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  <img src="https://raw.githubusercontent.com/InternLM/lmdeploy/0be9e7ab6fe9a066cfb0a09d0e0c8d2e28435e58/resources/lmdeploy-logo.svg" width="450"/>
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# 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:

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- [Inference](#inference)
- [Evaluation](#evaluation)
- [Service](#service)

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## Inference

Trying the following codes, you can perform the batched offline inference with the quantized model:

```python
from lmdeploy import pipeline
from lmdeploy.messages import TurbomindEngineConfig
from lmdeploy.vl import load_image

model = 'OpenGVLab/InternVL-Chat-V1-5-AWQ'
image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
backend_config = TurbomindEngineConfig(model_format='awq')
pipe = pipeline(model, backend_config=backend_config, log_level='INFO')
response = pipe(('describe this image', image))
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 OpenGVLab/InternVL-Chat-V1-5-AWQ --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).