Maximilian: Now besides uploading and handling images,
which, again, must be supported by the model you're using
to be an option,
besides doing that, you can also attach other files,
like for example, PDF, plain text or Word documents.
Now, at least at the point of time
where I'm recording this,
an info pop-up opens
if I choose the option to attach a file.
And in this info pop-up,
I get informed about the file types I can add,
and that, at least right now when I'm recording this,
I can upload up to five files
with a maximum combined size of 30 megabytes.
Now, of course, when you are viewing this video,
you might no longer get this message.
The limits also might have changed.
But in general, when working with those local models,
just slamming dozens of large files
all into this one chat session
typically won't be a good idea.
If you wanna process multiple files,
you should do it in chunk.
The Context window size will also matter,
but that's something I'll get back to later.
In general, what LM Studio will try to do
with any files you give it
is it will try to load the content of this file
into the chat history invisibly behind the scenes,
as if you would have copy and pasted
that content into the history.
If that's not possible because the Context window,
and again, I'll get back to this, is not sufficient,
so if the model as it's currently configured
would not be able to handle that amount of input,
if that's not possible,
LM Studio will actually try to split the content
into smaller chunks for you
and try to retrieve the relevant chunks
based on the prompt you sent to the model.
That's a technique called Retrieval Augmented Generation,
and LM Studio will do it for you when uploading files.
And again, the Context window part
is something we'll explore later.
Now here for this demo,
I will upload a pretty small PDF document.
It's just a very simple made up financial report PDF
that includes a bunch of information
about made up financials and numbers.
But, of course,
we can also use locally running large language models,
like this one here, for summarizing documents like this.
And indeed that is a task they're really, really good at.
I will also say that the Gemma models, for example,
do support quite large Context windows
if you configure them appropriately,
so they can handle quite a lot of information,
and for example, summarize it for you.
So for example, here I'll ask Gemma 3,
the 12 billion parameters model again,
to summarize this document
and extract the key insights and financials for me,
and that again, as I just mentioned,
is one core use case for using large language models
that are running locally on your system.
They're really good at that,
and of course, the huge advantage
is that you don't need to share information like this,
which potentially might, of course, be confidential
or which you simply don't wanna share with everyone,
with OpenAI or any other provider.
Instead, you can perform tasks like this
locally on your system
by simply uploading documents to LM Studio
and then working on them
with your favorite large language model,
in this case, the Gemma 3 model.