autogen/website/blog/2023-07-14-Local-LLMs/index.md

---
title: Use AutoGen for Local LLMs
authors: jialeliu
tags: [LLM]
---
**TL;DR:**
We demonstrate how to use autogen for local LLM application. As an example, we will initiate an endpoint using [FastChat](https://github.com/lm-sys/FastChat) and perform inference on [ChatGLMv2-6b](https://github.com/THUDM/ChatGLM2-6B).

## Preparations

### Clone FastChat

FastChat provides OpenAI-compatible APIs for its supported models, so you can use FastChat as a local drop-in replacement for OpenAI APIs. However, its code needs minor modification in order to function properly.

```bash
git clone https://github.com/lm-sys/FastChat.git
cd FastChat
```

### Download checkpoint

ChatGLM-6B is an open bilingual language model based on General Language Model (GLM) framework, with 6.2 billion parameters. ChatGLM2-6B is its second-generation version.

Before downloading from HuggingFace Hub, you need to have Git LFS [installed](https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage).

```bash
git clone https://huggingface.co/THUDM/chatglm2-6b
```

## Initiate server

First, launch the controller

```bash
python -m fastchat.serve.controller
```

Then, launch the model worker(s)

```bash
python -m fastchat.serve.model_worker --model-path chatglm2-6b
```

Finally, launch the RESTful API server

```bash
python -m fastchat.serve.openai_api_server --host localhost --port 8000
```

Normally this will work. However, if you encounter error like [this](https://github.com/lm-sys/FastChat/issues/1641), commenting out all the lines containing `finish_reason` in `fastchat/protocol/api_protocol.py` and `fastchat/protocol/openai_api_protocol.py` will fix the problem. The modified code looks like:

```python
class CompletionResponseChoice(BaseModel):
    index: int
    text: str
    logprobs: Optional[int] = None
    # finish_reason: Optional[Literal["stop", "length"]]

class CompletionResponseStreamChoice(BaseModel):
    index: int
    text: str
    logprobs: Optional[float] = None
    # finish_reason: Optional[Literal["stop", "length"]] = None
```


## Interact with model using `oai.Completion` (requires openai<1)

Now the models can be directly accessed through openai-python library as well as `autogen.oai.Completion` and `autogen.oai.ChatCompletion`.


```python
from autogen import oai

# create a text completion request
response = oai.Completion.create(
    config_list=[
        {
            "model": "chatglm2-6b",
            "base_url": "http://localhost:8000/v1",
            "api_type": "open_ai",
            "api_key": "NULL", # just a placeholder
        }
    ],
    prompt="Hi",
)
print(response)

# create a chat completion request
response = oai.ChatCompletion.create(
    config_list=[
        {
            "model": "chatglm2-6b",
            "base_url": "http://localhost:8000/v1",
            "api_type": "open_ai",
            "api_key": "NULL",
        }
    ],
    messages=[{"role": "user", "content": "Hi"}]
)
print(response)
```

If you would like to switch to different models, download their checkpoints and specify model path when launching model worker(s).

## interacting with multiple local LLMs

If you would like to interact with multiple LLMs on your local machine, replace the `model_worker` step above with a multi model variant:

```bash
python -m fastchat.serve.multi_model_worker \
    --model-path lmsys/vicuna-7b-v1.3 \
    --model-names vicuna-7b-v1.3 \
    --model-path chatglm2-6b \
    --model-names chatglm2-6b
```

The inference code would be:

```python
from autogen import oai

# create a chat completion request
response = oai.ChatCompletion.create(
    config_list=[
        {
            "model": "chatglm2-6b",
            "base_url": "http://localhost:8000/v1",
            "api_type": "open_ai",
            "api_key": "NULL",
        },
        {
            "model": "vicuna-7b-v1.3",
            "base_url": "http://localhost:8000/v1",
            "api_type": "open_ai",
            "api_key": "NULL",
        }
    ],
    messages=[{"role": "user", "content": "Hi"}]
)
print(response)
```

## For Further Reading

* [Documentation](/docs/Getting-Started) about `autogen`.
* [Documentation](https://github.com/lm-sys/FastChat) about FastChat.