AI Bootcamp: LLM Fine Tuning and Deployment, organized by SCB 10X and @float16cloud, has successfully concluded. The event shared crucial knowledge and techniques on fine-tuning and practically deploying Large Language Models (LLMs). . 👉Key Takeaway - Led by Typhoon: 5 tips for fine-tuning models effectively . 1. Spend over 80% of time on data preparation (quality is fundamental) 2. Create at least two evaluation datasets: one must be entirely unseen data 3. During fine-tuning, use train and eval sets to monitor overfitting 4. Evaluate the model both before and after fine-tuning to confirm real improvement 5. Review and refine chat templates—system prompts, instruction formats, etc.—good templates yield more accurate and better-performing responses . 👉Key Takeaway - Led by Float16: 3 techniques for making LLMs work in actual software development . 1. Choose file formats that match the purpose: • .safetensors → for HuggingFace—separates model weights and tokenizer from architecture • .gguf → for llama-cpp, Ollama, LM-studio—easier to use 2. Select formats appropriately: • safetensors for fine-tuning • gguf for inference (especially with OpenAI API Compatibility) 3. Structured Output (grammar) improves output quality: • Use xgrammar, outlines, guidance to shape responses • JSON mode for precise function calling • Define custom grammar rules for SQL, multiple-choice selections, and unique formats #SCB10X #Typhoon #Float16 #Bootcamp #AIBootCamp