This NEW AI AGENT is INSANE! (FREE!)🤯

The crazy part is how they trained it. They didn't use any human tech supervision. They use pure reinforcement learning. The model basic a brand new AI agent just dropped and it's absolutely destroying models 263 times bigger than it. This thing runs on your phone. No cloud needed. No internet required. And it's beating models that usually need massive servers. We're talking about a tiny 3 billion parameter model that's outperforming giants. This is actually insane. Let me show you why this changes everything. Hey, if we haven't met already, I'm the digital avatar of Julian Goldie, CEO of SEO agency Goldie Agency. Whilst he's helping clients get more leads and customers, I'm here to help you get the latest AI updates. Julian Goldie reads every comment, so make sure you comment below. So, here's

what just happened. Liquid AI just released something called LFM2 2. 6bx. Now, I know that sounds like a bunch of random letters and numbers, but trust me, this is a game changer. This model has only 2. 6 6 billion parameters. That's tiny in the AI world. Most powerful models have hundreds of billions of parameters, but this little guy is punching way above its weight class. Ellie taught itself to get better, and the results are honestly shocking. On instruction, following benchmarks, it beats Deep Seek R1, which is 263 times larger. Let that sink in. A model you can run on your phone is beating a model that needs a data center. Now, why does this matter for you? because this runs locally on your device, your phone, your laptop, no sending data to the cloud, no monthly subscription fees, no waiting for API calls. Everything happens right there on your device. And it's fast. Like really fast. We're talking up to two times faster than other similar models on CPU.

Let me break down what this thing can actually do. First, it's built specifically for agent tasks. What does that mean? It means this model can have actual conversations. It can chain reasoning together. It can use tools. It can follow complex multi-step instructions. Think about setting up automated workflows for your business. Customer service bots, content generation systems, data extraction pipelines, all running locally on your hardware. Here's a practical example. Let's say you want to automate content creation for the AI profit boardroom. You could use this model to generate social media posts about AI automation. It understands context. It follows instructions precisely. And because it's running on your device, you can process unlimited content without paying per token. No API costs adding up. You own the model. You control everything. The

benchmark scores are honestly ridiculous for a 3 billion parameter model. On GSM8K, which test grade school math problems, it scores 82. 4%. That's better than Llama 3. 23B, which only gets 75. 2%. On complex reasoning tasks, it scores 79. 6%. of three billion parameter models struggle to break 71%. This isn't just a little better. This is significantly better across the board. And before we go further, if you want to learn how to save time and automate your business with AI tools like this one, you need to check out the AI profit boardroom. We're showing people exactly how to use these cutting edge models to build real automation systems. How to set up workflows that actually work. How to integrate AI into your business without the tech headaches. This is the kind of tool that can transform how you work, but you need to know how to use it properly. That's what we teach inside the boardroom. Link in the description. Right back to this insane model. The

model has a 32,000 token context window. That means it can process really long documents. You can feed it entire articles, long customer emails, product descriptions, research papers, and it'll understand all of it. Then it can extract information, summarize key points, answer questions about the content, all while running on your laptop. Now, let me tell you about the architecture because this is actually fascinating. Most language models use standard transformer blocks. They're powerful, but they're slow on regular CPUs. Liquid AI built something different. They use a hybrid architecture with gated convolutions and grouped attention blocks. What does that mean in plain English? It means the model is designed from the ground up to be fast on the hardware you already own. No need for expensive GPUs. The model was trained on 10 to 12 trillion tokens across multiple languages. That's an absolutely massive training data set. Most three billion parameter models don't see nearly that much data. This gives it a much broader knowledge base. It understands context better. It can handle more complex tasks. And it works in eight different languages out of the box. Here's what I love about this. The

model supports tool calling natively. That means you can give it access to external functions, APIs, Python scripts, database queries, whatever you need. The model will figure out when to use these tools. It'll format the request properly. Then it'll integrate the results back into its response. This is huge for building actual AI agents that can interact with your systems. The instruction following capability is what really sets this apart. Most small models struggle with complex instructions. They miss steps. They forget context. They don't follow the format you specify. This model nails it. On ifbench, which specifically tests instruction following, it outperforms models hundreds of times larger. That means when you tell it to do something, it actually does it exactly how you asked for first time. And the speed is incredible because of that optimized architecture. You get three times faster training compared to earlier models. But more importantly, you get two times faster inference on CPU compared to similar models. What does that mean practically? It means when you send a prompt, you get your answer back almost instantly, even on a phone, even with long prompts. Now let's talk about knowledge and reasoning on MMLU which tests general knowledge across multiple domains. The model scores 64. 4%. That's better than most other 3 billion parameter models. It understands science, history, math, literature, technology, business, all the major knowledge domains. And on GPQA, which tests really hard physics and science questions, it reportedly hits around 42% accuracy. That's remarkable. Only giant models usually reach that level. The multilingual capability is another massive advantage. If you're working with international clients or global communities, this model handles eight languages natively. English obviously, but also Spanish, French, German, Chinese, and more. It's not just translating either. It actually understands the nuances of each language, cultural context, idioms, technical terminology. Let me tell you

how to actually get this and use it. The model is completely free and open source. You can download it right now from HuggingFace. Just search for liquid AAI/LFM2-2. 6B-XP. It comes in safe tenses format. There's also a GGUF version if you want to use it with llama. cpp. The setup is straightforward. You need hugging face transformers version 4. 55 or higher. The code to load it is super simple. You basically just load the model with automodel for causal lm. Point it at the model name. Tell it to use bfloat 16 precision and you're ready to go. The official documentation has sample scripts that show you exactly how to run it. Even if you're not a hardcore developer, you can get this running in minutes. Liquid AI also has a playground where you can test the model in your browser. Just go to playground. lquid. ai. You can try it out before downloading anything. See how it performs. Test it with your specific use cases. They also have a mobile app called Apollo that runs these models locally on your phone. And if you want to customize it for your specific needs, you can fine-tune it. There are example notebooks on GitHub showing how to do supervised fine-tuning. You can adapt it to your industry, your writing style, your specific tasks. And because it's only 2. 6 billion parameters, finetuning is actually affordable. You don't need massive GPU clusters. Now, here's the

bottom line. This model represents a massive shift in AI. We're moving from cloud dependent systems to edge devices, from expensive API calls to free local inference, from black boxes to transparent controllable systems, and the performance is actually better in many cases. This isn't a compromise. This is the future. So go download this model, test it out, see what it can do for your specific needs. And if you want help implementing tools like this to automate your business and save hours every single week, check out the AI Profit Boardroom. We show you exactly how to take these cutting edge tools and turn them into practical business automation. No fluff, no theory, just real systems that save you time. Link in the description. And if you want the full process, SOPs, and over 100 AI use cases like this one, join the AI success lab. It's our free AI community. Links in the comments and description. You'll get all the video notes from there, plus access to our community of 40,000 members who are crushing it with AI. I'll see you in the next one.