Ollama

Interact with your Ollama models using LLM.

Parameters

An Ollama LLM interface can have the following parameters:

Parameter	Type	Description
`temperature`	float	The temperature parameter for the model.
`top_p`	float	The top-p parameter for the model.
`num_predict`	int	The number of tokens to predict.
`top_k`	int	The top-k parameter for the model.

Usage

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

Create a config.yaml in the same directory your code is in.

src
1. yourPythonCode.py
2. yourPyNotebook.py
3. config.yaml

Define your Ollama provider and models inside the config.yaml file.

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

If you are not sure about any of these parameters, you can just leave them as 0

Create your llm instance.

llm = LLM('ollama/{YOUR_MODEL}')

Optional: You can add your parameters as follows:

llm = LLM('ollama/model', 
temperature= ...,
num_predict= ...,
top_p= ...,
top_k= ...,)

You are done setting up your Ollama LLM!

What’s next?

LLM.chat()

Learn how to send messages and recieve responses next!

Tool calling Agent

Learn how to build a tool calling agent using llmstudio.

Azure Openai

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

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