LLM Fine-Tuning 21: Google Gemini Fine-Tuning Masterclass using Vertex AI | Supervised Finetuning

In this video, we deep dive into LLM Fine-Tuning with Google Gemini on Vertex AI. You’ll learn what fine-tuning is, why it matters, and how Supervised Fine-Tuning (SFT) works in real-world enterprise use cases. We cover: What is LLM Fine-Tuning & why it’s useful Gemini fine-tuning approaches (Full vs Adapter vs SFT) Supported Gemini models (Gemini Pro, Flash, Flash-Lite) Dataset formats (JSONL) for text, image, audio, video & documents Preference Tuning vs Supervised Fine-Tuning Cost, pricing, limits & best practices Practical Gemini fine-tuning using Vertex AI Console & Python SDK This video is perfect for GenAI engineers, ML engineers, and LLM practitioners working on production-grade AI systems. 🚀 Learn how to fine-tune Gemini models correctly and avoid common mistakes. Tags / Keywords: Gemini Fine Tuning, Vertex AI, LLM Fine Tuning, Supervised Fine Tuning, Gemini Flash, Gemini Pro, Google Vertex AI, GenAI, Adapter Tuning, LoRA, ML Engineering Material & Resources: https://github.com/sunnysavita10/Complete-LLM-Finetuning/tree/main/LLM%20Fine-Tuning-21-GEMINI-Finetuning 🔔 Like, Share & Subscribe to stay updated with the full LLM fine-tuning playlist. Got questions or topic requests? Drop a comment below 👇. 📌 Keywords Covered: #LLMFineTuning #LLMQuantization #GPTQ #PTQ #QAT #AWQ #GGUF #GGML #llamaCpp #DeepLearning #NeuralNetworkOptimization #Transformers #HuggingFace #LangChain #LangGraph #RAG #AdvancedRAG #AIAgents #AgenticAI #GenerativeAI #LLMTutorial #AIProjects #AIForDevelopers #TransferLearning #FineTuning #PretrainedModels #OpenSourceAI #LLM #MachineLearning #ArtificialIntelligence #AITutorial #Python #Chatbot #StructuredOutput #PromptEngineering #TextGeneration #Embedding #LLMWorkflow #SunnyAI #YouTubeLearning #AIautomation #AIForBusiness #EndToEndTutorial #LLMFineTuning #DomainSpecificLLM #HuggingFace #SunnySavita #AIProjects #LangChain #FineTuningTutorial #AIML #LoRA #QLoRA #AITraining #CustomLLM #PDFData #preference alignment #