---
license: mit
---
<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
For lmdeploy v0.5.0, please configure the chat template config first. Create the following JSON file `chat_template.json`.
```json
{
    "model_name":"internlm2",
    "meta_instruction":"你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。",
    "stop_words":["<|im_start|>", "<|im_end|>"]
}
```

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

```python
from lmdeploy import pipeline
from lmdeploy.model import ChatTemplateConfig
from lmdeploy.vl import load_image

model = 'OpenGVLab/InternVL2-2B-AWQ'
chat_template_config = ChatTemplateConfig.from_json('chat_template.json')
image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
pipe = pipeline(model, chat_template_config=chat_template_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/InternVL2-2B-AWQ --backend turbomind --model-format awq --chat-template chat_template.json
```

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).