Localai

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Experiment with AI models locally with zero technical setup, powered by a native app designed to simplify the whole process. No GPU required!

How Localai can help you:

Enables experimenting with AI models offline and in private.
Simplifies the AI model management process.
Facilitates integrity verification of downloaded models using BLAKE3 and SHA256.
Allows for easy setup of a local streaming server for AI inferencing.

Why choose Localai: Key features

Compact and efficient with a Rust backend.
No GPU required, making it accessible on standard hardware.
Free and open-source, encouraging transparency and community development.

Who should choose Localai:

Developers and researchers working with AI models.
Individuals interested in AI experimentation without heavy technical setup.
Organizations looking for a lightweight, efficient solution for AI model management and inferencing.

About Localai

Website

https://www.localai.app

Release Date

March 2024

Pricing

Free

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Liquid AI Launches LEAP: A New Platform for On-Device AI Development

Liquid AI introduces LEAP, a software development kit for integrating small language models into mobile applications, along with Apollo, an iOS app for testing these models locally. This marks a significant step towards privacy-preserving, efficient AI on consumer hardware.

2 Sources

Wed, 16 Jul, 12:16 AM UTC

Open Deep Research : Powerful Fully Local ChatGPT Agent (Open Source)

What if you could harness the power of advanced AI research tools without relying on proprietary platforms or sacrificing control over your data? Imagine a system that combines the sophistication of innovative technologies like ChatGPT agents with the freedom and flexibility of open source software -- deployed entirely on your local machine. Enter Open Deep Research, a new platform that redefines how researchers, developers, and innovators approach complex challenges. With its multimodal capabilities and customizable workflows, this tool is poised to disrupt the status quo, offering a powerful alternative to closed systems like OpenAI's ChatGPT agent or Gemini. But is it truly as fantastic as it sounds? World of AI provide an overview of how Open Deep Research enables users to tackle intricate problems with precision, adaptability, and autonomy. From its real-time web search functionality to its intuitive dashboard creation tools, the platform is packed with features designed to streamline workflows and enhance productivity. You'll discover how its structured, three-step process ensures clarity and actionable results, and why its open source nature makes it a future-ready solution for diverse industries. Whether you're an academic, a developer, or an AI enthusiast, this platform offers a glimpse into what's possible when innovation meets accessibility. Could this be the tool that transforms your research process? Let's find out. Open Deep Research is equipped with a range of features that simplify and enhance the research process. These capabilities are designed to address the demands of modern research while maintaining adaptability and precision: These features make Open Deep Research a valuable tool for a wide array of applications, ranging from academic research to AI-driven projects, making sure that users can address complex challenges with ease. Open Deep Research employs a streamlined three-step workflow to ensure clarity, efficiency, and precision throughout the research process. This structured approach is designed to guide users from the initial planning stages to actionable outcomes: This workflow not only enhances productivity but also ensures that each stage of the research process is methodical and results-oriented, making it easier to achieve meaningful insights. Here are additional guides from our expansive article library that you may find useful on local AI models. Setting up Open Deep Research requires some technical preparation, but its flexibility and potential for customization make the effort worthwhile. To get started, follow these steps: Once the platform is installed, users can take advantage of its extensive customization options. Agents, tools, and models can be tailored to meet specific research requirements, making Open Deep Research a versatile solution for a wide range of use cases. Open Deep Research's adaptability makes it suitable for a variety of applications, catering to professionals across different fields. Some of its key use cases include: These applications highlight the platform's ability to handle diverse tasks, making it a valuable asset for researchers, developers, and industry professionals alike. The Studio UI serves as the central hub for managing agents, tracking logs, and configuring settings within Open Deep Research. This intuitive interface is designed to accommodate both novice and advanced users, offering a range of tools to enhance usability: By providing a user-friendly interface, Open Deep Research ensures that users can navigate the platform efficiently, regardless of their technical expertise. One of the standout features of Open Deep Research is its flexibility and extensibility. The platform supports integration with a variety of APIs and tools, allowing users to expand its capabilities as their needs evolve. Whether you are working on a specific project or developing a custom AI-driven application, Open Deep Research can adapt to your requirements, making sure long-term utility and relevance. This adaptability makes it an ideal choice for professionals seeking a scalable and future-ready research platform. By using its open source nature, users can continuously refine and enhance the platform to meet emerging challenges and opportunities. Open Deep Research stands out as a powerful, open source solution for conducting advanced research and automating complex tasks. With its multimodal integration, configurable tools, and local deployment options, it offers a reliable and flexible platform for researchers, developers, and innovators. By following its structured workflow and using its extensive features, users can streamline their research processes, enhance accuracy, and generate actionable insights tailored to their objectives. This platform not only simplifies the research journey but also enables users to achieve meaningful outcomes in an increasingly data-driven world.

Geeky Gadgets

Wed, 23 Jul, 8:20 AM UTC

3 local LLM workflows that actually save me time

Local large language models are having a moment. Much as I love online AI models like Perplexity, I care about my data and have been using local AI models to boost productivity. Over the past few months, I've been running these models entirely on my own machine, and not a massively powerful one at that, and it has completely transformed how I work. Using a local model via Ollama instead of relying on cloud services has many benefits. Chief among those, of course, is offline access and privacy. While there are many ways to amplify your productivity using AI, here are three ways I'm using a local LLM to boost mine. Knowledge management Your personal research assistant This one's a no-brainer, but one of my favorite use cases is as an accompaniment to my personal knowledge management app. While I'm big on Obsidian and Notion, a local LLM can go a long way towards helping you manage that information better. For example, I make a lot of meeting notes, articles, research documents, and, usually, I'd have to go through these manually. One document at a time. Now, I can feed all the documents into a tool like TL/DW or Obsidian Copilot and get it to process them using a large local language model. This lets me create summaries, pull out action points, and even have a conversation with my own documents for better learning. It's basically a cheat sheet for creating Cliff Notes, and I love it. Of course, there are the usual benefits, like retaining ownership of all the data, not just my documents, but also all the summarization data created by the LLM. This is data that I cannot afford to have on the internet as training data, so public LLMs are out of the question. Having my own LLM also lets me play around with formats, like creating flashcards for learning or getting them to create a summarized digest of multiple research papers that I can read up on using my Kindle. It's the best personal research assistant I've ever had. Content management Saving time without losing control and context The second workflow is perhaps the most controversial. As a journalist, I'm vehemently opposed to the idea of using AI slop. But I do feel that AI has a place in modern workflows to accelerate productivity. Drafting a boilerplate contract email? Get the local LLM to create a template for you. Need to create an outline for talking points to cover in a pitch deck? LLM to the rescue. Or how about asking it to poke holes in a write-up? Grammar checks. Ideating headlines. Making sure the best SEO practices have been followed. These are all tasks that an LLM excels at. It doesn't need creativity; it just needs the model to hit check marks. As in the case of knowledge management, this is original data that I do not want on the internet, so a local LLM works best. Yes, a local LLM is nowhere near as good at text generation, but I don't need it to be. On average, I save at least ten to fifteen minutes in research and basic outlining because I can get the LLM to tell me if I've hit all the relevant talking points in my first draft. Home automation When Home Assistant gets an AI brain I love Home Assistant. It's the center of my smart home. But as powerful as Home Assistant is, it truly comes into its own when you pair it with an LLM. With recent updates, Home Assistant has made the task incredibly easy, which means I can get it to generate everything from routines to automations. When in a pinch, I've asked the local LLM to create a boilerplate YAML file that I can further work with for my dashboard. This is data I absolutely do not want exposed to the internet, so the ability to connect with an Ollama instance on my computer offers me both features and privacy. It's a win-win situation in my books. Adding a local LLM is the ultimate productivity hack Of course, there are dozens of other ways to use local LLMs to your advantage, but for me, these three use-cases have stood out the most to accelerate my productivity. It's transformed how I handle everything from my personal life to my home and my work. I'm now able to gather information faster than I ever could, and wrangle that into a form that is best suited for the task at hand. The local LLM stands by me as I write and pokes holes in my work to make it better, while also offering basic grammar checking to make my editor's life easier. And to make my life easier, it taps into my smart home to offer more contextual advice. All of which to say, adding a local LLM to my daily setup has been the best productivity boost I could've hoped for. Not only am I faster than ever, but it also gives me more time and mental space to focus on everything else beyond work.

XDA-Developers

Mon, 1 Sept, 2:24 PM UTC

I put Pinokio 2 to the test -- it's now easier than ever to run AI on your computer

One of the unsung heroes of the open-source AI arena is Pinokio, a pre-packager that offers instant installs of AI tools on PC, Mac, or Linux. Unofficially dubbed 'the Steam of AI applications', the two huge benefits of using this tool are the cross-platform versatility, and the literal one-click installation routines. The latest 2.1.14 version of Pinokio, released last week, gives some nice speed and usability upgrades which are worth checking out. One of the best parts of the product is the growing number of official and community-created 'scripts' that can be used to install AI goodies on your computer, ranging from image background removers to AI comic makers and beyond. This includes Open WebUI, a web service that lets you run models like Meta's Llama 3.1 and chat to it like you would ChatGPT. The first thing to mention is you'll need a pretty beefy computer set up to get things working properly. Even with a decent graphics card and fast CPU, you can be waiting a long time for the local AI to do its stuff -- especially for some of the more sophisticated apps. For example, trying to create an animated talking image on my Nvidia RTX 4060 Windows PC, with 24GB of RAM and an Intel i7 processor showed a completion time of over a day using the Hallo app. Quick Windows tip: use Task Manager to shut down all open apps and free up RAM, before you fire up a Pinokio app. You can thank me later. In the end, I searched the cool integrated Discover app catalog to find something more modest to test out, and ended up installing Stable Audio Tools. This gives a nice, easy Gradio front end to the Stable Audio platform. This is where Pinokio shows its class. From deciding to select the app to getting underway took less than five minutes. Once you select Download -- Install, all of the cumbersome tech stuff is done for you, no worries about dependencies or which version of Python to install. Wonderful stuff! Installation of the required files races across the screen as the environment is set up in a flash. It's a testament to the Pinokio community's testing and debugging that it all works so well. Once I had the app installed, it was simply a matter of downloading my model of choice, entering a prompt and waiting for a few minutes to generate some nice AI music clips on my local PC. Very impressive. One massive feature which is probably a game changer for this local AI sector, is the ability to use Pinokio to share and access local apps from a remote computer like your laptop. To use it, click the Local Share menu option once you've got an app running, then use the Share option in the app to either share the app on your local WiFi network or out on the Internet. You'll need to restart the app, but you'll then be able to access the app from anywhere using this share button. Just click on the QR code to get the address on the device, and you'll find yourself in the remote Pinokio app. It's a superb way to access a beefier AI computer at home, say, while you're out and about with your laptop. You may ask why go to the bother with all this, when there are numerous other commercial applications around that run faster, better and with more power. Well, it's all about control. Those of us who are more than a little nervous about surrendering our data and privacy to cloud service providers like OpenAI or Adobe Firefly, are starting to vote with our feet. Yes, using open-source AI tools locally can be a hassle, and yes it's much much slower unless you have a gigantic computer rig in your bedroom. But at the end of the day, using these apps on our own devices gives us total control over the input, output and other aspects of the process. It's also very rewarding when you succeed in generating something for free using your computer and a local LLM, and realize how far open-source AI has come in only a few months. Pinokio gives homebrew open-source AI a golden opportunity to spread to a wider audience. People who would never think of visiting Github or installing Python can now enjoy a lot of the AI treats that have previously been reserved for the brave and geeky. Here's a tip of the hat to @cocktailpeanut and the rest of the team!

Tom's Guide

Tue, 30 Jul, 4:08 PM UTC

How to run AI for free on macOS, Linux and Windows

Struggling with high data costs and privacy concerns while using cloud-based AI models? You're not alone. Many users face these challenges daily. But what if there was a way to run AI models locally on your computer, whether it's macOS, Linux, or Windows? Installing AI models on your local machine offers significant privacy and cost benefits. This guide by Corbin Brown will walk you through setting up AI models on the major operating systems, using the Llama 3.1 model as an example. By following these steps, you can run AI models without internet connectivity, ensuring your data remains private and you avoid ongoing costs. To begin, download and install the AI model on your operating system. Whether you are using macOS, Linux, or Windows, the process involves similar steps. First, download the Llama 3.1 model from a trusted source. Ensure you have the necessary permissions to install software on your machine. The installation process typically involves the following steps: By carefully following each step and ensuring all dependencies are met, you can successfully install the Llama 3.1 model on your local machine, regardless of your operating system. Here are a selection of other articles from our extensive library of content you may find of interest on the subject of running artificial intelligence large language models locally on your local network or workstation. Running AI models locally provides two significant advantages: enhanced privacy and cost savings. When you process data on your own device, you avoid sending sensitive information over the internet, ensuring that your data remains secure and confidential. This is particularly important for applications dealing with personal, financial, or proprietary data. Moreover, by running AI models locally, you eliminate the need for cloud-based services, which can be costly over time. Cloud providers often charge based on usage, and the costs can quickly add up, especially for resource-intensive AI tasks. By running models on your own hardware, you can avoid these ongoing expenses and have more control over your computing resources. Before installing an AI model, it's crucial to consider the hardware requirements. Different models have varying demands in terms of processing power and memory. For the Llama 3.1 model, a machine with at least 16GB of RAM and a modern CPU is recommended. This ensures that the model can run efficiently and process data in a reasonable amount of time. For more complex or larger AI models, a dedicated GPU may be necessary to achieve optimal performance. GPUs are particularly well-suited for parallel processing tasks commonly found in AI workloads. If you plan to work with demanding models, investing in a machine with a suitable GPU can significantly speed up processing times. When you first install an AI model, you may interact with it through a command-line interface in a terminal. While this is functional, transitioning to a user-friendly web interface can greatly enhance the usability and accessibility of the model. Tools like Flask or Django, which are web frameworks for Python, can help you create a web-based interface for your AI model. This allows users to input data, adjust parameters, and view results through a more intuitive and visually appealing interface. A web interface also makes it easier to share the model's functionality with others, as they can access it through a web browser. Managing AI models locally involves selecting the right model for your specific needs and keeping it up to date. As new versions of models are released, it's important to update your local installation to take advantage of the latest features, performance improvements, and bug fixes. You may also need to switch between different models depending on the task at hand. Some models are better suited for certain types of data or applications, so having the flexibility to choose the most appropriate model is valuable. By managing your models locally, you have full control over which versions and variations you use. Running AI models offline opens up a wide range of practical applications across various domains. Some common use cases include: These are just a few examples of the many practical applications of running AI models offline. As AI continues to advance, the possibilities for local AI deployment will only expand. By following this guide, you can successfully install and run AI models on your local machine, leveraging the benefits of privacy and cost savings. Whether you are using macOS, Linux, or Windows, the steps outlined here will help you get started with offline AI applications. With the ability to run models locally, you can unlock the full potential of AI while maintaining control over your data and resources.

Geeky Gadgets

Thu, 8 Aug, 10:04 AM UTC

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