Free Download Develop AI, Fine tuning and Full Local AI KnowledgePublished 4/2026
Created by Hasan Kanjo
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels |
Genre: eLearning |
Language: English |
Duration: 8 Lectures ( 1h 49m ) |
Size: 1.57 GB
✓ Evaluate and select the right open-weight models (Gemma, Llama 3, Qwen) based on their specific use case and hardware constraints
✓ Deploy and manage local LLMs efficiently using backend engines like llama.cpp and Ollama
✓ Optimize model performance for consumer GPUs (e.g., 8GB VRAM limits) using quantization techniques (GGUF, AWQ)
✓ Set up intuitive user interfaces, integrating backend engines with frontends like AnythingLLM for chat and document interaction.
✓ Fine Tune AI Models
Requirements● AI Skills
DescriptionThe AI revolution isn't just happening in the cloud anymore-it is happening right on your desktop. But if you have ever tried to get open-source models running locally, you know it can feel like a maze of complex GitHub repositories, broken dependencies, and confusing file formats.
This course cuts through the noise. It is designed as a practical, hands-on guide to show you exactly how to download, install, and seamlessly use leading open-weight models like Google's Gemma, Alibaba's Qwen, and Meta's Llama directly on your own machine. We focus heavily on getting you up and running quickly, so you spend less time troubleshooting and more time actually interacting with your local AI.
We will walk through the entire setup process step-by-step. You'll learn how to leverage powerful backend engines like Ollama and llama.cpp to run models efficiently, even if you are working with the constraints of a standard consumer laptop GPU with limited VRAM. From there, we move beyond the command line. We'll set up graphical interfaces like AnythingLLM, turning your raw models into a highly usable, private workspace where you can chat with your own documents and manage your daily workflows without ever sending a single byte of data to a third-party server.
Finally, once you are comfortable using these models day-to-day, we tackle customization. Fine-tuning used to require expensive server rentals, but we will use Unsloth to make the process incredibly fast and hardware-friendly. You will learn the exact steps to take a base model, feed it your own instruction data, and train it to perform specialized tasks right on your own machine. Whether you are looking to build private tools for your own productivity or just want to master the local LLM ecosystem, this course gives you the exact blueprint to make it happen.
Who this course is for■ Everyone
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