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
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.