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
| Property | Details |
|---|---|
| Description | Discover, download, and run local LLMs. |
| Provider Route on LiteLLM | lm_studio/ |
| Provider Doc | LM Studio ↗ |
| Supported OpenAI Endpoints | /chat/completions, /embeddings, /completions |
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['LM_STUDIO_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 LM Studio model with the LiteLLM Proxy Server
- 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
- Start the proxy
$ litellm --config /path/to/config.yaml
- Send Request to LiteLLM Proxy Server
- OpenAI Python v1.0.0+
- curl
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.
Embedding
from litellm import embedding
import os
os.environ['LM_STUDIO_API_BASE']="http://localhost:8000"
response = embedding(
model="lm_studio/jina-embeddings-v3",
input=["Hello world"],
)
print(response)
Structured Output
LM Studio supports structured outputs via JSON Schema. You can pass a pydantic model or a raw schema using response_format.
LiteLLM sends the schema as { "type": "json_schema", "json_schema": {"schema": <your schema>} }.
from pydantic import BaseModel
from litellm import completion
classBook(BaseModel):
title:str
author:str
year:int
response = completion(
model="lm_studio/llama-3-8b-instruct",
messages=[{"role":"user","content":"Tell me about The Hobbit"}],
response_format=Book,
)
print(response.choices[0].message.content)