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LM Studio

https://lmstudio.ai/docs/basics/server

tip

We support ALL LM Studio models, just set model=lm_studio/<any-model-on-lmstudio> as a prefix when sending litellm requests

API Key​

# env variable
os.environ['LM_STUDIO_API_BASE']
os.environ['LM_STUDIO_API_KEY'] # optional, default is empty

Sample Usage​

from litellm import completion
import os

os.environ['LM_STUDIO_API_BASE'] = ""

response = completion(
    model="lm_studio/llama-3-8b-instruct",
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in Boston today in Fahrenheit?",
        }
    ]
)
print(response)

Sample Usage - Streaming​

from litellm import completion
import os

os.environ['XAI_API_KEY'] = ""
response = completion(
    model="lm_studio/llama-3-8b-instruct",
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in Boston today in Fahrenheit?",
        }
    ],
    stream=True,
)

for chunk in response:
    print(chunk)

Usage with LiteLLM Proxy Server​

Here's how to call a XAI model with the LiteLLM Proxy Server

  1. Modify the config.yaml 

    model_list:
      - model_name: my-model
        litellm_params:
          model: lm_studio/<your-model-name>  # add lm_studio/ prefix to route as LM Studio provider
          api_key: api-key                 # api key to send your model

  1. Start the proxy 

    $ litellm --config /path/to/config.yaml

  2. Send Request to LiteLLM Proxy Server

    import openai
    client = openai.OpenAI(
        api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
        base_url="http://0.0.0.0:4000" # litellm-proxy-base url
    )

    response = client.chat.completions.create(
        model="my-model",
        messages = [
            {
                "role": "user",
                "content": "what llm are you"
            }
        ],
    )

    print(response)

Supported Parameters​

See Supported Parameters for supported parameters.