Скачать или смотреть Fine-tune & Chat with LLMs Locally: MLX + Ollama + Open WebUI Tutorial (Apple Silicon) 🚀

Looking to leverage your Mac's powerful Apple Silicon for AI development? You're in the right place! In this comprehensive, step-by-step tutorial, I'll show you how to fine-tune Large Language Models (LLMs) using the cutting-edge MLX framework - Apple's breakthrough for native AI development on Mac.

Whether you're a seasoned ML engineer or just starting your AI journey, this tutorial breaks down the entire process: from setting up your development environment to deploying a production-ready model. You'll learn how to fine-tune models using LoRA, convert them to the efficient GGUF format, and deploy them using popular tools like Ollama and Open WebUI.

What makes this tutorial special:
• Native Apple Silicon optimization - no more CPU/GPU compatibility issues
• Complete workflow from setup to deployment
• Real-world examples and practical applications
• Performance optimization tips and best practices
• Detailed explanations of each step

⏱️ Full Tutorial Breakdown:
00:23 Main Agenda
00:58 Github Repository Display

🔧 Part I - Setting up the coding environment
01:56 Proposed Fine-tuning Process
03:04 VS Code Installation
03:19 Python Installation
03:44 Home Brew Installation
04:03 Git Installation
04:29 Ollama Installation & Usage
05:47 Open WebUI Installation & Usage
07:10 Further Installations in Jupyter Notebook

📊 Part II
09:53 Create/Import Data for Fine-tuning

🤖 Part III - Train, Test & Validate the LLM
12:59 Step 3.1 - Variables/Arguments Definition
14:26 Step 3.2 & 3.3 - Huggingface Model Download & Conversion to MLX
16:05 Step 3.4 - LoRA Fine-tuning under the MLX Framework
18:31 Step 3.5 - Testing the adapters performance
19:28 Step 3.6 - Model Validation and Inferencing

⚡️ Part IV - Fuse adapters with LLM and convert to GGUF
22:18 Step 4.1 - Fuse adapters to trained model and saving locally
24:08 Step 4.2 - Testing and Inferencing the fused model through terminal
25:28 Step 4.3 - Testing and Inferencing the fused model through Python API
26:50 Step 4.4 - Exporting the fused model to Huggingface
27:32 Step 4.5 - Converting the fused model to GGUF format (online & locally)
29:48 Step 4.6 - Create a model file for the GGUF model
31:28 Step 4.6 - Export the GGUF model to ollama and inferencing
33:01 Step 4.6 - Inferencing the ollama GGUF model in Open WebUI

33:49 Recap
34:10 Outro

🎯 What You'll Learn:
• Complete environment setup on macOS
• Creating fine-tuning datasets
• Training with LoRA adapters in MLX
• Fusing adapters with base models
• Converting models to GGUF format
• Deploying on Ollama and Open WebUI

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🔗 General Links & Resources:
⚫OurGithub Repo used in this video : https://github.com/MoAshour93/MLX_Fin...
⚫ Construction Data Analytics Github Repository: https://github.com/MoAshour93/Constru...
⚫My personal Github page: https://github.com/MoAshour93
⚫Our Website: www.apcmasterypath.co.uk
⚫All APC Mastery Path Blogposts: https://www.apcmasterypath.co.uk/blog...
⚫Personal Linkedin Page:   / mohamed-ashour-0727  
⚫APC Mastery Path Linkedin Page:   / apc-mastery-path  

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