Example with openAI client and locally deployed LLM returns "Failed to deserialize the JSON body into the target type: tool_choice: data did not match any variant of untagged enum ToolTypeDeserializer"

[paguilomanas]

• This is actually a bug report.
• I am not getting good LLM Results
• I have tried asking for help in the community on discord or discussions and have not received a response.
• I have tried searching the documentation and have not found an answer.

What Model are you using?

• gpt-3.5-turbo
• gpt-4-turbo
• gpt-4
• Other -> "Llama-3.1-70B-Instruct" locally deployed with TGI

Describe the bug
I am trying the very first example of Instructor library but with a locally deployed model endpoint. I get the following error: InstructorRetryException: Failed to deserialize the JSON body into the target type: tool_choice: data did not match any variant of untagged enum ToolTypeDeserializer at line 1 column 161

To Reproduce

python 3.10
instructor 1.6.3

import openai
import instructor
from pydantic import BaseModel


class User(BaseModel):
    name: str
    age: int


instructor_client = instructor.from_openai(openai.OpenAI(
                                base_url="http://10.10.78.13:8080/v1",
                                api_key="unused",
))

user = instructor_client.chat.completions.create(
    model="Llama-3.1-70B-Instruct",
    messages=[
        {"role": "user", "content": "Create a user"},
    ],
    response_model=User,
)

print(user.name)
print(user.age)

Expected behavior
I would expect the user to be a valid BaseModel json schema but the instructor_client.chat.completions.create() call is not working.

Other details

• My local model endpoint works perfectly when calling it using the openAI client like this:

client = openai.OpenAI(
            base_url="http://10.10.78.13:8080/v1",
            api_key="unused"
            )
response = self.client.chat.completions.create(
               model="Llama-3.1-70B-Instruct",
               messages=[{"role": "user", "content": "Create a user"}])

print(response.choices[0].message.content)

• If the problem is related with my locally deployed model, can someone show me how to use instructor library with a locally deployed model? The final objective for it to work is to apply it when using deepeval framework.