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Use AutoGen for Local LLMs

· 3 min read
Jiale Liu

TL;DR: We demonstrate how to use autogen for local LLM application. As an example, we will initiate an endpoint using FastChat and perform inference on ChatGLMv2-6b.

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.

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.

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

Initiate server​

First, launch the controller

python -m fastchat.serve.controller

Then, launch the model worker(s)

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

Finally, launch the RESTful API server

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

Normally this will work. However, if you encounter error like this, 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:

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.

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": "openai",
            "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": "openai",
            "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:

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:

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": "openai",
            "api_key": "NULL",
        },
        {
            "model": "vicuna-7b-v1.3",
            "base_url": "http://localhost:8000/v1",
            "api_type": "openai",
            "api_key": "NULL",
        }
    ],
    messages=[{"role": "user", "content": "Hi"}]
)
print(response)

For Further Reading​