LFM2-2.6B-Exp : Build ANYTHING! 🤯

This tiny AI model just destroyed a giant 671 billion parameter monster and it's only 2. 6 billion parameters. That's like a Chihuahua beating a Great Dane in a fight. Liquid AI just changed the game. Let me show you how. 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. All right, so something crazy just happened in the AI world. A 2. 6 billion parameter model just beat a 671 billion parameter model. That's not a typo. The small model won and it won big. This is LFM22. 6bx

from Liquid AI, and it's about to flip everything you know about AI models upside down. Here's why this matters. For years, everyone said bigger is better. More parameters equals smarter AI. But Liquid AI said no. They proved that how you train a model matters way more than size. And they did it with pure reinforcement learning. No fluff, no shortcuts, just results. Let me break down what makes this model so special. First, the name LFM2 2. 6BX. LFM stands for liquid foundation model. The 2. 6B means 2. 6 billion parameters and X means experimental. This is their testing ground for a new way to train AI. Now, here's where it gets

interesting. Most AI models are trained in three steps. Step one is pre-training on tons of text. Step two is supervised fine-tuning where humans teach it. Step three is reinforcement learning from human feedback. That's the normal way. But Liquid AI threw that playbook out the window. They went straight to pure reinforcement learning. No human preference tuning, just verifiable reward signals. What does that mean in plain English? It means the model learns from clear right and wrong answers, not from human opinions, not from preference data, from actual verifiable results, and that changes everything. The model follows instructions exactly. It doesn't add extra fluff. It doesn't hallucinate as much. It does exactly what you tell it to do. This is huge for agents. If you're building AI automation workflows, this model is gold. Let me show you the benchmark that shocked everyone. There's

a test called ifbench. It measures instruction following. How well does the model do what you ask? LFM22. 6b XP scored higher than Deepseek R1. Deepseek R1 has 671 billion parameters. That's 263 times bigger. Think about that. A model 263 times smaller, just one. That's like a smart car beating a semi-truck in a race. It shouldn't happen, but it did. And here's why. The architecture is smart. Liquid AI uses something called ELIV convolutions. These handle short range reasoning really well. They also use grouped query attention. This makes the model faster and uses less memory. So you get the best of both worlds. Local reasoning from convolutions, global reasoning from attention. It's a hybrid approach and it works incredibly well. The whole thing was trained on 10 trillion tokens. That's a massive amount of data. But the key difference is how they trained it after that initial phase. Now let me

talk about what this model is actually good at. Number one, agent orchestration. If you're building AI agents that call tools and make decisions, this is your model. Number two, rag pipelines. That's retrieval, augmented generation. Basically, pulling information and summarizing it. Number three, data extraction. Pulling structured data from unstructured text. Number four, multi-turn conversations. The model stays on track across long chats. Number five, creative writing with constraints. Give it rules and it follows them perfectly. And number six, reasoning tasks, math problems, logic puzzles, step-by-step thinking. All right, I'm about to show you some real examples, but first, let me tell you about something cool. At the AI profit boardroom, we're using small, efficient models like this one to automate business workflows. Think about it. You could run this model on your own hardware. No API costs, no data leaving your system, and you can build custom AI agents that run all day, every day. We're teaching people how to set up AI automation systems that save hours every single day, like using models like this to automatically respond to customer emails or extract data from reports and put it straight into spreadsheets or create content briefs for your team. All automated, all using small powerful models that you control. If you want to learn how to actually use AI to save time and grow your business, check out the AI profit boardroom. Link is in the description. Now, let's get into the

demos. Demo number one, instruction following stress test. I'm going to give this model a crazy prompt with lots of rules. Let's say I ask it to write a product description for the AI profit boardroom, but with strict rules, must be exactly 50 words. Must mention automation three times. Must end with a question. Must use simple language. But LFM2 nails it. Every single rule followed perfectly. No extra fluff. No apologies. Just the output you asked for. This is what pure RL training does. It teaches the model to follow instructions like a laser. Demo number two, a gentic workflow. Let's say you're building an AI system that plans tasks, executes them, and validates results. This is a classic agent loop. Most models drift after a few turns. They start adding unnecessary commentary. They lose track of the structure. But LFM2 stays locked in. You give it a role as a planner, it plans. an executive, it executes. No confusion, no drift. This is perfect for business automation. Demo number three, math and reasoning. Let's give it a chain of thought math problem. Something like calculate the ROI of an ad campaign with a bunch of variables. LFM2 breaks it down step by step. Clean reasoning, no mistakes. And here's the cool part. It doesn't overexlain. It just shows the work and gives you the answer. This is gold for business analysis. You can feed it data and get clear, logical breakdowns. No fluff, no confusion, just clean, accurate results. And because it's trained with verifiable rewards, the math actually checks out. It's not just making stuff up. Now, let's talk

about how you actually run this thing. The model is available on HuggingFace. Search for LiquidAI/LFM22. 6b XP. You can download it and run it locally. Hardware requirements surprisingly low. You can run this on a consumer GPU, even a laptop if you use quantization. For agents, use a lower temperature setting that makes outputs more predictable. For rag pipelines, pair it with a vector database. Now it has access to way more knowledge. This is the perfect control model for agentic systems. It's small enough to run fast, smart enough to handle complex instructions, and stable enough to not go haywire. It's one of the best models you can get, and it's free to use. Final

verdict: LFM22. 6b XP proves that scale is not the only path forward. Training strategy matters more than parameter count. And reinforcement learning is the key to building better agents. If you're building AI automation systems, you need to test this model. If you're tired of paying for expensive API calls, you need to test this model. If you want AI that actually follows instructions, you need to test this model. It's not perfect, but it's a glimpse of where AI is heading. Smaller, smarter, and more controllable. This is what Liquid AI is all about. They're designing models for how agents actually behave in production. Not just for benchmarks, not just for demos, for real world use. All right, before we wrap up, if you want to learn how to actually implement AI automation in your business, join the AI profit boardroom. We teach you how to use tools like this to save time, cut costs, and scale your operations. Link is in the description. And if you want the full process, SOPs, and 100 plus 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. Comment below if you're going to try this model. Let me know what you build with it, and I'll see you in the next one.