litellm/docs/my-website/docs/providers/lm_studio.md

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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
  1. 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)
curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Authorization: Bearer sk-1234' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "my-model",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ],
}'

Supported Parameters

See Supported Parameters for supported parameters.