How to Build a Private AI on Your Own PC (PEFT & DPO Guide). Post-training is rapidly becoming a critical phase of enterprise AI development. To get reliable output from an AI model, organizations must align its terminology (e.g., abbreviation) to fit their specific use cases. But getting started shouldn't require heavy computing resources—you can quickly train an open-source model right on your local device. In this tutorial, we sit down with the ASAP_DPO_Finetuning Cloudera AMP to demonstrate exactly how to align a language model to specific industry standards—in this case, Oil & Gas abbreviations. By leveraging Direct Preference Optimization and Quantized Low-Rank Adaptation, we show you how to effectively post-train a model on a standard desktop or laptop. This highly efficient local workflow serves as the perfect starting point: you can build and validate your private AI quickly, then easily scale up later with larger datasets, bigger models, and additional remote GPUs. If you’re looking to get hands-on with AI post-training quickly and securely, this guide provides the exact starting point you need. *Want to learn more about Cloudera AMPs? Check out Cloudera’s official website.* https://cloudera.github.io/Applied-ML-Prototypes/#/community *Stay in touch with Mason:* Mason Jung on Linkedin: https://www.linkedin.com/in/minseok-jung/ Stay up to date on the latest episodes by liking and subscribing to Cloudera's YouTube channel. https://www.youtube.com/@ClouderaInc Join the Cloudera Community to learn more! 👉https://community.cloudera.com *Links & Resources* Check out the ASAP_DPO_Finetuning Repository: https://github.com/masonjung/ASAP_DPO_Finetuning Explore Cloudera AMPs: https://cloudera.github.io/Applied-ML-Prototypes/#/community *Other AI-post training AMPs* Supervised Finetuning (SFT): https://github.com/cloudera/CML_AMP_Finetune_Foundation_Model_Multiple_Tasks Group Relative Policy Optimization (GRPO): https://github.com/cloudera/CML_AMP_Med_Reasoning *Get Started* 🔹 Watch Demos: https://www.cloudera.com/products/cloudera-data-platform/cdp-demos.html?utm_medium=social-organic&utm_source=youtube&keyplay=cross&utm_campaign=Other---AlwaysOn-GLOBAL-WS-Website-CDP-Demo-Request&cid=7012H000001Z3MrQAK&utm_content=youtube-cta 🔹 Customer Success Stories: https://www.cloudera.com/customers.html?utm_medium=social-organic&utm_source=youtube&keyplay=AI-Anywhere&utm_campaign=Other---Did-You-Know-Fraud-detection-customer&cid=UNGATED&utm_content=youtube-cta 🔹 Read the Cloudera blog: https://www.cloudera.com/blog.html Chapters: 00:00 Intro: What is AI Post-Training? 01:08 Overview: Problem Statement & ML Accelerators (AMPs) 02:15 What are Cloudera AMPs? (Machine Learning Blueprints) 03:12 Pre-training vs. Post-training explained 04:07 Training Architecture: Small Language Models & QLoRA 07:51 Configuration: Hyperparameters and Secret Tokens 09:57 Importing Modules and Project Dependencies 11:27 Starting the DPO Training (Step-by-Step) 13:04 Comparing Results: Pre-trained vs. Post-trained Output 14:03 Summary and Scaling Up to Enterprise Infrastructure *Connect with Cloudera* Subscribe to stay ahead of the curve with the latest in data strategy, open architectures, and enterprise AI innovations. https://www.cloudera.com LinkedIn ► https://www.linkedin.com/company/cloudera Facebook ► https://www.facebook.com/cloudera/ X ► https://x.com/cloudera Podcast on Spotify ► https://open.spotify.com/show/102S8zoZR6nmZV0HxZlxZu #Cloudera #GenerativeAI #FineTuning #AIResearch #privateai #Llama3 #MachineLearning

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