• views: 56
• date: 14 February 2025
• posted by: AD-TEAM

Build AI Chatbots, Deploy Local AI Models, and Create AI-Powered Apps Without Cloud APIs using DeepScaleR-1.5B AI Model

What you'll learn

Set up DeepScaler & Ollama for local AI model execution.

Run AI models locally without relying on cloud APIs.

Build an AI-powered chatbot using DeepScaler & FastAPI.

Develop an AI Math Solver that handles complex equations.

Deploy DeepScaler models via REST APIs for real-world use.

Integrate DeepScaler with Gradio for web-based AI tools.

Benchmark DeepScaler vs OpenAI models in performance tests.

Requirements

Basic Python knowledge (helpful but not mandatory).

Familiarity with command-line tools (Linux/Mac/Windows Terminal).

A computer with at least 8GB RAM (higher for better performance).

Ollama installed (we'll guide you through setup).

Internet connection for downloading models (offline use supported after setup).

Interest in AI, LLMs, and model deployment.

No prior deep learning or ML experience needed!

Description

Mastering DeepScaler and Ollama is your gateway to building, fine-tuning, and deploying AI models locally without relying on expensive cloud APIs. This hands-on course will teach you how to harness the power of open-source AI to create intelligent applications that run on your own machine. You will learn how to work with DeepScaler, a fine-tuned version of DeepSeek-R1-Distilled-Qwen-1.5B, optimized for math reasoning, code generation, and AI automation, while Ollama enables seamless local AI model deployment for efficient and cost-effective AI applications.This course is designed to take you from beginner to advanced AI development. You will start by setting up DeepScaler and Ollama on Mac, Windows (WSL), or Linux. From there, you will learn how to run AI models locally, eliminating the need for cloud-based APIs. You will build a fully functional AI chatbot using DeepScaler and deploy it via FastAPI. You will also develop an AI-powered Math Solver that can solve complex equations in real time.A major focus of the course is fine-tuning DeepScaler using LoRA and QLoRA. You will train DeepScaler on custom datasets to improve responses and adapt the model to domain-specific tasks such as finance, healthcare, and legal analysis. The course will also guide you through building an AI-powered Code Assistant, which can generate, debug, and explain code efficiently.One of the most important aspects of working with AI models is optimization for low-latency responses. You will learn how to improve AI inference speed and compare DeepScaler's performance against OpenAI's o1-preview. The course will also introduce Gradio, a tool that allows you to create interactive AI-powered web applications, making it easier to deploy and test AI models in a user-friendly interface.This course is ideal for AI developers, software engineers, data scientists, and tech enthusiasts who want to learn how to deploy AI models without cloud dependencies. It is also a great choice for students and beginners who want to get started with local AI model development without requiring prior deep learning experience.Unlike traditional AI development, local AI deployment provides greater privacy, security, and control. With DeepScaler and Ollama, you will be able to run AI models on your device without incurring API costs or depending on third-party cloud services. This enables real-time AI-powered applications with faster response times and better efficiency.By the end of this course, you will have multiple AI-powered applications running locally with models fine-tuned for specific use cases. Whether you are building a chatbot, a math solver, a code assistant, or an AI-powered automation tool, this course will provide you with the knowledge and hands-on experience needed to develop, fine-tune, and deploy AI models effectively.No prior AI experience is required. If you are interested in LLM fine-tuning, AI chatbot development, code generation, AI-powered automation, and local AI model deployment, this course will give you the tools and expertise to master these skills.

Overview

Section 1: Introduction to DeepScaler & Ollama

Lecture 1 What is DeepScaleR: Fine-Tuned version of Deepseek-R1-Distilled-Qwen-1.5B?

Lecture 2 What is Ollama? How to use it with DeepScaleR

Lecture 3 Setting Up the Environment

Lecture 4 Python Basics

Section 2: Building AI Applications with DeepScaler

Lecture 5 Project 1: AI-Powered Math Solver

Lecture 6 Project 2: AI Chatbot using DeepScaler

AI Developers & Engineers – Learn to build, fine-tune, and deploy AI models efficiently.,Data Scientists – Optimize AI-powered applications for local and enterprise use.,Software Developers – Integrate AI into projects without relying on cloud APIs.,AI Enthusiasts & Researchers – Experiment with local AI models and custom fine-tuning.,Tech Startups & Entrepreneurs – Deploy AI chatbots, assistants, and automation tools.,Students & Learners – Gain hands-on experience with AI model deployment.,Makers & Hobbyists – Run AI models on personal devices for fun projects.




   
   
   

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