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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.