Azure

Interact with your Azure models using LLM.

Parameters

An Azure LLM interface can have the following parameters:

Parameter	Type	Description
temperature	float	The temperature parameter for the model.
max_tokens	int	The maximum number of tokens to generate.
top_p	float	The top-p parameter for the model.
frequency_penalty	float	The frequency penalty parameter for the model.
presence_penalty	float	The presence penalty parameter for the model.

Usage

Here is how you setup an interface to interact with your Azure models.

OpenAI Models

1. Create a config.yaml file in the same directory as your code.

   1. 📁 src
      1. 🐍 PythonCode.py
      2. 🐍 PyNotebook.ipynb
      3. 📄 config.yaml
2. Define your Azure OpenAI provider and models inside the config.yaml file.

       providers:
           azure:
               id: azure
               name: Azure
               chat: true
               embed: true
               models:
                   YOUR_MODEL: <- Replace with your model name
                       mode: chat
                       max_tokens: ...
                       input_token_cost: ...
                       output_token_cost: ...
   

   If you are not sure, you can leave max_tokens, input_tokens and the other parameters as 0
3. Create your llm instance.

   llm = LLM('azure/YOUR_MODEL',
           api_key = YOUR_API_KEY,
           api_endpoint = YOUR_ENDPOINT,
           api_version = YOUR_API_VERSION)
   

4. Optional: You can add your parameters as follows:

   llm = LLM('azure/model', 
   temperature= ...,
   max_tokens= ...,
   top_p= ...,
   frequency_penalty= ...,
   presence_penalty= ...)
   

   You are done setting up your Azure LLM!

What’s next?

LLM.chat()

Learn how to send messeges and recieve responses next!

Tool calling Agent

Learn how to build a tool calling agent using llmstudio.