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Урок 1.
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1- Welcome
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Урок 2.
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2- Prerequisites
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Урок 3.
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3- What You'll Learn
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Урок 4.
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4- Setting Up Your Development Environment
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Урок 5.
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1- Introduction
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Урок 6.
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2- Rise of AI Engineering
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Урок 7.
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3- What are Large Language Models?
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Урок 8.
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4- What Can You Do With Language Models?
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Урок 9.
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5- Understanding Tokens and Context Window
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Урок 10.
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6 - Counting Tokens
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Урок 11.
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7- Choosing the Right Model
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Урок 12.
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8- Understanding Model Settings
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Урок 13.
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9- Calling Models
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Урок 14.
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1- Introduction
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Урок 15.
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2- Setting Up Bun
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Урок 16.
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3- Creating the Project Structure
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Урок 17.
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4- Creating the Backend
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Урок 18.
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5- Managing OpenAI API Key
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Урок 19.
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6- Creating the Frontend
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Урок 20.
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7- Connecting the Frontend and Backend
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Урок 21.
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8- Running Both Apps Together
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Урок 22.
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9- Setting Up TailwindCSS
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Урок 23.
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10- Setting Up Shadcn
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Урок 24.
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11- Formatting Code with Prettier
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Урок 25.
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12- Automating Pre-Commit Checks with Husky
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Урок 26.
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1- Introduction
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Урок 27.
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2- Building the Backend
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Урок 28.
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2.1- Building the Chat API
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Урок 29.
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2.2- Testing the API
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Урок 30.
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2.3- Managing Conversation State
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Урок 31.
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2.4- Input Validation
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Урок 32.
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2.5- Error Handling
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Урок 33.
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3- Refactoring the Chat API
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Урок 34.
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3.1- Extracting Conversation Repository
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Урок 35.
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3.2- Extracting Chat Service
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Урок 36.
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3.3- Extracting Chat Controller
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Урок 37.
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3.4- Extractring Routes
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Урок 38.
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4- Building the Frontend
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Урок 39.
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4.1- Building the ChatBot Component
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Урок 40.
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4.2- Handling Form Submission
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Урок 41.
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4.3- Calling the Backend
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Урок 42.
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4.4- Rendering the Messages
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Урок 43.
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4.5- Styling Messages
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Урок 44.
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4.6- Rendering Markdown Text
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Урок 45.
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4.7- Adding a Typing Indicator
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Урок 46.
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4.8- Auto-Scrolling to the Latest Message
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Урок 47.
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4.9- Improving Copy Behaviour
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Урок 48.
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4.10- Improving the Look and Feel
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Урок 49.
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4.11- Handling Errors
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Урок 50.
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5- Refactorings
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Урок 51.
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5.1- Extracting TypingIndicator Component
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Урок 52.
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5.2- Extracting ChatMessages Component
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Урок 53.
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5.3- Extracing ChatInput Component
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Урок 54.
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5.4- Recap
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Урок 55.
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1- What is Prompt Engineering
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Урок 56.
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2- Anatomy of a Good Prompt
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Урок 57.
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3- Providing Context
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Урок 58.
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4- Controlling the Output Format
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Урок 59.
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5- Providing Examples
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Урок 60.
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6- Handling Errors and Edge Cases
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Урок 61.
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7- Reducing Hallucinations
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Урок 62.
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8- Refining Prompts
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Урок 63.
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9- Improving Chatbot Responses
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Урок 64.
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10- Adding Sound Effects
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Урок 65.
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1- Introduction
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Урок 66.
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2- Setting Up the Database
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Урок 67.
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2.1- Setting Up MySQL
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Урок 68.
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2.2- Setting Up Prisma
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Урок 69.
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2.3- Defining the Prisma Schema
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Урок 70.
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2.4- Running Migrations
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Урок 71.
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2.5- Refining the Prisma Schema
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Урок 72.
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2.6- Populating the Database with Realistic Data
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Урок 73.
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3- Building the Backend
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Урок 74.
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3.1- Creating the API to Fetch Reviews
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Урок 75.
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3.2- Refactoring- Separation of Concerns
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Урок 76.
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3.3- Creating an API for Summarizing Reviews
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Урок 77.
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3.4- Generating Summaries
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Урок 78.
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3.5- Refactoring- Extracting the LLM Logic
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Урок 79.
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3.6- Refactoring- Extracting the Prompt
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Урок 80.
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3.7- Storing the Summary
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Урок 81.
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3.8- Handling Regeneration
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Урок 82.
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3.9- Handling Edge Cases
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Урок 83.
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3.10- Fetching the Summary
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Урок 84.
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4- Building the Frontend
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Урок 85.
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4.1- Displaying Reviews
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Урок 86.
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4.2- Displaying Star Ratings
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Урок 87.
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4.3- Displaying Loading Skeletons
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Урок 88.
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4.4- Handling Errors
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Урок 89.
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4.5- Introducing Tanstack Query
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Урок 90.
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4.6- Displaying the Summary
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Урок 91.
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4.7- Triggering Summary Generation
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Урок 92.
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4.8- Displaying Loading Skeletons
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Урок 93.
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4.9- Handling Errors
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Урок 94.
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4.10- Refactoring with Mutations
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Урок 95.
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4.11- Refactoring for Readability
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Урок 96.
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4.12- Extracting the API Layer
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Урок 97.
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1- Introduction
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Урок 98.
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2- Why Use Open-Source Models
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Урок 99.
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3- Finding Open-Source Models
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Урок 100.
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4- Calling Hugging Face Models
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Урок 101.
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5- Choosing the Right Model For the Job
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Урок 102.
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6- Running Models Locally
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Урок 103.
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7- Using Hugging Face Models with Ollama
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Урок 104.
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8- Calling Ollama Models
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Урок 105.
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1- Course Wrap Up