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
Other Models

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

📁 src
1. 🐍 PythonCode.py
2. 🐍 PyNotebook.ipynb
3. 📄 config.yaml

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

Create your llm instance.

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

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!

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

📁 src
1. 🐍 PythonCode.py
2. 🐍 PyNotebook.ipynb
3. 📄 config.yaml

Define your Azure 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

Create your llm instance.

llm = LLM('azure/YOUR_MODEL',
           api_key = YOUR_API_KEY,
           base_url = YOUR_ENDPOINT)

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.

Get Started

LLM

How to

Parameters

Usage

What’s next?

LLM.chat()

Tool calling Agent

​Parameters

​Usage

​What’s next?

LLM.chat()

Tool calling Agent

Parameters

Usage

What’s next?