# Build and Automate Anything with LFM2.5-1.2B-Thinking!

## Метаданные

- **Канал:** Julian Goldie SEO
- **YouTube:** https://www.youtube.com/watch?v=ed-HpUXZoT8
- **Дата:** 26.01.2026
- **Длительность:** 8:09
- **Просмотры:** 3,161
- **Источник:** https://ekstraktznaniy.ru/video/9889

## Описание

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Run Reasoning AI on Your Phone with LFM 2.5 1.2b Thinking

Discover LFM 2.5 1.2b, a revolutionary local AI model that brings high-level reasoning and automation to your pocket with zero cloud costs. Learn how to build privacy-first workflows and leverage its step-by-step thinking process for your business.

00:00 - Intro to Local Reasoning AI
00:53 - What is LFM 2.5 1.2b?
01:32 - Understanding Reasoning Traces
02:12 -

## Транскрипт

### Intro to Local Reasoning AI []

Build and automate anything with LFM2. 5 1. 2b thinking. This AI model runs on your phone. No cloud needed under 900 m byte of memory. It thinks before it answers and it's completely free. Most AI needs powerful servers. This one fits in your pocket. It shows you its thinking process step by step. You can see exactly how it solves problems. It's like having a reasoning engine anywhere you go. No internet required. No API costs. just pure ondevice intelligence. And today I'm showing you how to use it. This is going to change how you automate your business. Let's dive in. 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. [snorts] Today we're

### What is LFM 2.5 1.2b? [0:53]

talking about something wild. A reasoning AI model that runs entirely on your device, your phone, your laptop, even a Raspberry Pi. No internet required, no cloud servers, no API costs. This is LFM2. 51. 2b thinking from liquid AI. And it's a gamecher for automation. Let me explain why. Most AI models need massive servers. You send a request, it goes to the cloud, the cloud processes it, then sends it back. That takes time, it costs money, and you need internet. LFM2. 51. 2b. Thinking is different. It runs locally on your device under 900 megabytes of RAM. That's smaller than most apps on your phone. And here's the crazy part.

### Understanding Reasoning Traces [1:32]

It doesn't just spit out answers. It shows you its thinking process step by step, like watching someone solve a math problem. You see the work, you see the logic, you see how it got to the answer. This is called reasoning traces. And it makes the AI way more reliable because you can audit the thinking. You can catch mistakes. You can trust the output. Now, let me show you what this thing can actually do. First, it's a beast at math and logic problems. We're talking calculus, algebra, word problems, complex reasoning tasks. On the math 500 benchmark, it scores 88. On GSMATK, it scores 85. 6. Those are insane numbers for a model this small. It beats models with way more parameters. Models that are twice its size. But here's where it gets really interesting. This

### Building Local Automations [2:12]

model is built for automation. It can use tools. It can plan workflows. It can orchestrate tasks. Think about running a community or business. You need to automate member on boarding, send welcome emails, schedule calls, add people to your CRM, update spreadsheets. Normally, you'd need complex workflow tools or expensive automation software. With LFM2. 51. 2b thinking, you can build these workflows yourself. The model can reason through the steps. It can decide what action comes next. It can handle exceptions. It can adapt to different scenarios. And it does all of this without needing cloud access. Everything happens on your device. Your data stays private. Your automations run offline. No latency, no downtime, no dependency on external servers. Let me give you a practical example. Say you want to automate content creation for your business. You feed the model a topic like AI automation tools for small businesses. The model starts thinking. It breaks down the topic into subtopics. It outlines key points. It structures the content logically. You can see each step of its reasoning. Then it produces the final output. But here's the key difference from other models. You saw how it got there. You can verify the logic. You can adjust the process. You can refine the automation. Another example, customer support automation. Someone sends a question to your business. The model reads the question. It thinks through possible answers. It considers context from previous conversations. It decides on the best response. It drafts the reply. You see the entire reasoning chain. You know why it chose that answer. You can trust it to handle real customer interactions.

### Model Specs & Compatibility [3:41]

The model has 1. 7 billion parameters with a context window of 32,568 tokens. That means it can process really long inputs, long documents, long conversations, long workflows, is text only, which keeps it lean and fast. Pure reasoning power optimized for tool use and instruction following. Here's what makes this really special. You can deploy this anywhere. Your phone, your laptop, an edge device, a robot. The deployment options are massive. You can run it with llama. cpp, mlx, vlm, ornx. runtime. You can use it with Olama for a simple command line interface. The model is available on hugging face right now in multiple formats. Day one compatibility across all major runtimes. Now, let me show you some real world use

### Top 5 Real-World Use Cases [4:21]

cases. First, mathematical tutoring. The model can solve complex math problems and show its work. Perfect for educational apps. The reasoning traces make it a teaching tool. Students see the process and learn how to think through problems themselves. Second, agentic automation. The model acts as the brain for autonomous agents. It can analyze data, reason about trends, decide what actions to take, and orchestrate API calls to other tools. You have full visibility into the automation logic. Third, privacy first applications. Because this runs on device, no data leaves your system. Huge for medical, legal, or financial use cases. You get powerful AI reasoning without compromising data security. Fourth, embedded systems and robotics. Put this inside robots or drones. It provides real-time decision-making with no latency. The robot can think through problems and adapt to changing conditions. Fifth, offline mobile assistance. Build apps that work without internet. Perfect for travelers or remote workers who need AI help in low connectivity environments. Now, let's

### How to Install & Get Started [5:27]

talk about how you actually get started with this. First, go to hugging face and search for liquid AI LFM 2. 5 1. 2b 2B thinking. Download the model weights in your preferred format. If you're using Olma, it's even easier. Just run pool LFM2. 5 thinking and you're ready to go. The model downloads. You can start using it immediately from the command line or you can integrate it into your Python scripts or your automation workflows. And if you want to learn how to save time and automate your business with AI tools like LFM 2. 5. 2b thinking, you need to check out the AI profit boardroom. We show you exactly how to implement these cutting edge AI models in your actual business workflows. How [clears throat] to build automations that save you hours every day. How to deploy AI tools that actually work. No theory, just practical implementations you can use today. The AI profit boardroom gives you the frameworks, the templates, and the community support to make AI automation real in your business. Link is in the description. So, here's what you do next. First, download the model from HuggingFace or install it via Olma. Second, experiment with simple reasoning tasks. Give it math problems. Give it logic puzzles. See how it thinks. Third, start building simple automations. Connect it to one tool, then two, then build a full workflow. Fourth, share what you build. The community is growing fast. People are finding creative use cases every day. The barrier to entry for AI automation just dropped to basically zero. You don't need expensive hardware. You don't need cloud budgets. You don't need technical expertise. You just need curiosity and willingness to experiment. This model makes advanced reasoning accessible to everyone. That's powerful. That's democratizing AI in a real way. 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. real people building real automations, sharing what works, helping each other solve problems is the best place to learn practical AI implementation. Look, and if you want to learn how to save time and automate your business with AI tools like LFM2. 5. 2b, thinking, you need to check out the AI profit boardroom. We show you exactly how to implement these cuttingedge AI models in your actual business workflows. [clears throat] How to build automations that save you hours every day.