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Model Management

Add new models + Get model info without restarting proxy.

In Config.yaml

- model_name: text-davinci-003
model: "text-completion-openai/text-davinci-003"
metadata: "here's additional metadata on the model" # returned via GET /model/info

Get Model Information - /model/info

Retrieve detailed information about each model listed in the /model/info endpoint, including descriptions from the config.yaml file, and additional model info (e.g. max tokens, cost per input token, etc.) pulled the model_info you set and the litellm model cost map. Sensitive details like API keys are excluded for security purposes.

curl -X GET "" \
-H "accept: application/json" \

Add a New Model

Add a new model to the list in the config.yaml by providing the model parameters. This allows you to update the model list without restarting the proxy.

curl -X POST "" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-d '{ "model_name": "azure-gpt-turbo", "litellm_params": {"model": "azure/gpt-3.5-turbo", "api_key": "os.environ/AZURE_API_KEY", "api_base": "my-azure-api-base"} }'

Model Parameters Structure

When adding a new model, your JSON payload should conform to the following structure:

  • model_name: The name of the new model (required).
  • litellm_params: A dictionary containing parameters specific to the Litellm setup (required).
  • model_info: An optional dictionary to provide additional information about the model.

Here's an example of how to structure your ModelParams:

"model_name": "my_awesome_model",
"litellm_params": {
"some_parameter": "some_value",
"another_parameter": "another_value"
"model_info": {
"author": "Your Name",
"version": "1.0",
"description": "A brief description of the model."

Keep in mind that as both endpoints are in [BETA], you may need to visit the associated GitHub issues linked in the API descriptions to check for updates or provide feedback:

Feedback on the beta endpoints is valuable and helps improve the API for all users.