Documentation Index
Fetch the complete documentation index at: https://docs.llmstudio.ai/llms.txt
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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
- 🐍 PythonCode.py
- 🐍 PyNotebook.ipynb
- 📄 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
- 🐍 PythonCode.py
- 🐍 PyNotebook.ipynb
- 📄 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.
