A few lines of Python is all it takes to get a model to use a calculator or even automate your hypervisor
Let's say you're tasked with solving a math problem like 4,242 x 1,977. Some of you might be able to do this in your head, but most of us would probably be reaching for a calculator right about now, not only because it's faster, but also to minimize the potential for error.
And so in this hands on, we'll be exploring some of the ways tool-calling can be used to augment the capabilities and address the limitations of LLMs.A system capable of running modest LLMs at 4-bit quantization. Any modern Nvidia graphics card or an AMD 7000-series GPU with at least 6GB of vRAM should do the trick. For Apple Silicon Macs, we recommend at least 16GB of memory.This guide also assumes that you already have the Ollama model runner installed.
By default, Open WebUI will populate the field with an example script that adds a variety of useful tools, including:Finally, give it a name like"BasicTools" and a brief description and click save.To put our tools to use, select your model and start a new chat, then press the"+" icon to the left of the message box and enable BasicTools. This will tell the model to use these tools wherever appropriate for the duration of your conversation.
The second element here is actually the docstring directly under the primary function. This doesn't just tell us what the function does, but it also provides the LLM instructions on what to do with the code.If you're struggling with your model being too conservative in its use of tools, we found it can be helpful to expand your docstrings with instructions on how, when, and in what format the model should utilize the tool.
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