Photo via editor Chatgpt
In the current era of products, the Gigantic Language (LLM) model, such as ChatgPT or Gemini, rely on these tools to boost our performance, helping in various tasks, such as answering questions, summary of documents, planning activities and many others. These tools have become part of our daily lives.
However, many of these products are hosted in the cloud and we must access them through their platform. In addition, each platform is usually restricted to its own reserved models and will not allow other LLM. That is why the Ollam application has been developed to lend a hand users who want to implement various LLM in the local environment.
Ollam has been around for some time and has already helped many users run language models locally. The latest update made it even stronger.
Let’s examine why the modern Ollamy application is becoming an indispensable tool for many users.
# The modern Ollamy application
As mentioned, Ollam It is an open source tool that supports LLM in a local environment without relying on hosting in the cloud. We can discover Which models are available On the OLLAMA website, download them and run directly using the application interface. The application works as a local model manager that hosts various models for free performance for us. Local application, as it benefits to the user, enabling him freedom while maintaining data privacy and reducing any delays.
The biggest development is that Ollam now acts as an independent GUI application, as opposed to only the command line. The times have passed necessarily Searching, installing and configuring the application of the external user interface (or your own writing), which can change Ollam into a more pleasant experience. Sure, you can still do it all, but there is no need.
Thanks to the modern update, the Ollam application has become more useful than ever and we will examine each of its functions individually.
The Ollam application allows users to exploit LLM locally by downloading the model to the local environment. You can interact with the model by selecting it and giving a prompt to get a result.
Ollam leads the history of conversation, allowing you to continue the conversation.
If the model is not yet available locally, the modern Ollam application automatically downloads it before making a prompt. This simplifies the user’s experience because they no longer have to download models separately before exploit.
Another modern feature is the ability to talk to your files. By dragging and dropping the file to the Ollam application, we can ask questions about its content.
The result is shown below, in which the model gains access to the file and makes a prompt based on the document provided.
If you want to process many larger documents, you can boost the length of the ollama context by settings. However, increasing the length of the context will require more memory to ensure stable performance.
The modern Ollamy functions are not restricted to text documents such as PDF and WORD. It also offers multimodal support, provided that the selected model can process different types of data. For example, we can exploit a model such as an image processing lama, as shown below.
The result is shown below.
Finally, the modern Ollam application can process code files for documentation. This function is particularly beneficial for programmers.
The result is shown below.
These are key modern functions that Ollam ensures to improve your performance. We encourage them to exploit them in your work.
# Wrapping
Ollam is an application that allows users to launch LLM in a local environment. It is a valuable tool for every user who wants to test different models and maintain data privacy. The modern version offers stronger functions, including useful user, uncomplicated downloading of the model, the possibility of chat with files, multimodal support and code processing.
I hope it helped!
Cornellius Yudha Wijaya He is a data assistant and data writer. Working full -time at Allianz Indonesia, he loves to share Python and data tips through social media and media writing. Cornellius writes on various AI topics and machine learning.