Calling Finetuned Models
OpenAI​
Model Name | Function Call |
---|---|
fine tuned gpt-4-0613 | response = completion(model="ft:gpt-4-0613", messages=messages) |
fine tuned gpt-4o-2024-05-13 | response = completion(model="ft:gpt-4o-2024-05-13", messages=messages) |
fine tuned gpt-3.5-turbo-0125 | response = completion(model="ft:gpt-3.5-turbo-0125", messages=messages) |
fine tuned gpt-3.5-turbo-1106 | response = completion(model="ft:gpt-3.5-turbo-1106", messages=messages) |
fine tuned gpt-3.5-turbo-0613 | response = completion(model="ft:gpt-3.5-turbo-0613", messages=messages) |
Vertex AI​
Fine tuned models on vertex have a numerical model/endpoint id.
- SDK
- PROXY
from litellm import completion
import os
## set ENV variables
os.environ["VERTEXAI_PROJECT"] = "hardy-device-38811"
os.environ["VERTEXAI_LOCATION"] = "us-central1"
response = completion(
model="vertex_ai/<your-finetuned-model>", # e.g. vertex_ai/4965075652664360960
messages=[{ "content": "Hello, how are you?","role": "user"}],
base_model="vertex_ai/gemini-1.5-pro" # the base model - used for routing
)
- Add Vertex Credentials to your env
!gcloud auth application-default login
- Setup config.yaml
- model_name: finetuned-gemini
litellm_params:
model: vertex_ai/<ENDPOINT_ID>
vertex_project: <PROJECT_ID>
vertex_location: <LOCATION>
model_info:
base_model: vertex_ai/gemini-1.5-pro # IMPORTANT
- Test it!
curl --location 'https://0.0.0.0:4000/v1/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: <LITELLM_KEY>' \
--data '{"model": "finetuned-gemini" ,"messages":[{"role": "user", "content":[{"type": "text", "text": "hi"}]}]}'