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Running LLMs Locally
Configuration
CLI
SDK (TypeScript)

Local LLM Server

You can serve local LLMs from LM Studio's Developer tab, either on localhost or on the network.

The server can be used both in OpenAI compatibility mode, or as a server for lmstudio.js.

In this article

Further reading

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Load and server LLMs from LM Studio

Pro Tip

As an LLM developer, you likely care about what inputs exactly go to the model. Utilize LM Studio's CLI to see the fully formatted prompt before it goes to the model.

Read more about lms log stream here.

OpenAI-like API endpoints

LM Studio accepts requests on several OpenAI endpoints and returns OpenAI-like response objects.

Supported endpoints

GET  /v1/models
POST /v1/chat/completions
POST /v1/embeddings
POST /v1/completions
See below for more info about each endpoint

Re-using an existing OpenAI client

Pro Tip

You can reuse existing OpenAI clients (in Python, JS, C#, etc) by switching up the "base URL" property to point to your LM Studio instead of OpenAI's servers.

Switching up the base url to point to LM Studio

Note: The following examples assume the server port is 1234
Python
from openai import OpenAI

client = OpenAI(
+    base_url="http://localhost:1234/v1"
)

# ... the rest of your code ...
Typescript
import OpenAI from 'openai';

const client = new OpenAI({
+  baseUrl: "http://localhost:1234/v1"
});

// ... the rest of your code ...
cURL
- curl https://api.openai.com/v1/chat/completions \
+ curl http://localhost:1234/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
-     "model": "gpt-4o-mini",
+     "model": "use the model identifier from LM Studio here",
     "messages": [{"role": "user", "content": "Say this is a test!"}],
     "temperature": 0.7
   }'

Endpoints overview

/v1/models

  • GET request
  • Lists the currently loaded models.
cURL example
curl http://localhost:1234/v1/models

/v1/chat/completions

  • POST request
  • Send a chat history and receive the assistant's response
  • Prompt template is applied automatically
  • You can provide inference parameters such as temperature in the payload. See supported parameters
  • See OpenAI's documentation for more information
  • As always, keep a terminal window open with lms log stream to see what input the model receives
Python example
# Example: reuse your existing OpenAI setup
from openai import OpenAI

# Point to the local server
client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio")

completion = client.chat.completions.create(
  model="model-identifier",
  messages=[
    {"role": "system", "content": "Always answer in rhymes."},
    {"role": "user", "content": "Introduce yourself."}
  ],
  temperature=0.7,
)

print(completion.choices[0].message)

/v1/embeddings

  • POST request
  • Send a string or array of strings and get an array of text embeddings (integer token IDs)
  • See OpenAI's documentation for more information
Python example
# Make sure to `pip install openai` first
from openai import OpenAI
client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio")

def get_embedding(text, model="model-identifier"):
   text = text.replace("\n", " ")
   return client.embeddings.create(input = [text], model=model).data[0].embedding

print(get_embedding("Once upon a time, there was a cat."))

/v1/completions

Heads Up

This OpenAI-like endpoint is no longer supported by OpenAI. LM Studio continues to support it.

Using this endpoint with chat-tuned models might result in unexpected behavior such as extraneous role tokens being emitted by the model.

For best results, utilize a base model.

Supported payload parameters

For an explanation for each parameter, see https://platform.openai.com/docs/api-reference/chat/create.

model
top_p
top_k
messages
temperature
max_tokens
stream
stop
presence_penalty
frequency_penalty
logit_bias
repeat_penalty
seed

Community

Chat with other LM Studio developers, discuss LLMs, hardware, and more on the LM Studio Discord server.