Update README.md

README.md

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----
-license: mit
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+---
+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/InternVL-Chat-V1-5-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).