mirror of https://github.com/InternLM/InternLM
60 lines
2.2 KiB
Markdown
60 lines
2.2 KiB
Markdown
|
# Inference by LMDeploy
|
||
|
|
|
||
|
|
English | [简体中文](lmdeploy_zh_cn.md)
|
||
|
|
|
||
|
|
[LMDeploy](https://github.com/InternLM/lmdeploy) is an efficient, user-friendly toolkit designed for compressing, deploying, and serving LLM models.
|
||
|
|
|
||
|
|
This article primarily highlights the basic usage of LMDeploy. For a comprehensive understanding of the toolkit, we invite you to refer to [the tutorials](https://lmdeploy.readthedocs.io/en/latest/).
|
||
|
|
|
||
|
|
## Installation
|
||
|
|
|
||
|
|
Install lmdeploy with pip (python 3.8+)
|
||
|
|
|
||
|
|
```shell
|
||
|
|
pip install lmdeploy
|
||
|
|
```
|
||
|
|
|
||
|
|
## Offline batch inference
|
||
|
|
|
||
|
|
With just 4 lines of codes, you can execute batch inference using a list of prompts:
|
||
|
|
|
||
|
|
```python
|
||
|
|
from lmdeploy import pipeline
|
||
|
|
pipe = pipeline("internlm/internlm2-chat-7b")
|
||
|
|
response = pipe(["Hi, pls intro yourself", "Shanghai is"])
|
||
|
|
print(response)
|
||
|
|
```
|
||
|
|
|
||
|
|
With dynamic ntk, LMDeploy can handle a context length of 200K for `InternLM2`:
|
||
|
|
|
||
|
|
```python
|
||
|
|
from lmdeploy import pipeline, TurbomindEngineConfig
|
||
|
|
engine_config = TurbomindEngineConfig(session_len=200000,
|
||
|
|
rope_scaling_factor=2.0)
|
||
|
|
pipe = pipeline("internlm/internlm2-chat-7b", backend_engine=engine_config)
|
||
|
|
gen_config = GenerationConfig(top_p=0.8,
|
||
|
|
top_k=40,
|
||
|
|
temperature=0.8,
|
||
|
|
max_new_tokens=1024)
|
||
|
|
response = pipe(prompt, gen_config=gen_config)
|
||
|
|
print(response)
|
||
|
|
```
|
||
|
|
|
||
|
|
For more information about LMDeploy pipeline usage, please refer to [here](https://lmdeploy.readthedocs.io/en/latest/inference/pipeline.html).
|
||
|
|
|
||
|
|
## Serving
|
||
|
|
|
||
|
|
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-7b
|
||
|
|
```
|
||
|
|
|
||
|
|
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
|
||
|
|
```
|
||
|
|
|
||
|
|
Alternatively, you can test the server's APIs oneline through the Swagger UI at `http://0.0.0.0:23333`. A detailed overview of the API specification is available [here](https://lmdeploy.readthedocs.io/en/latest/serving/restful_api.html).
|