{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"The Good Tech Companies ","title":"Fine-Tuning LLMs: A Comprehensive Tutorial","html":"","width":"100%","height":180,"duration":857,"description":"\n This story was originally published on HackerNoon at: https://hackernoon.com/fine-tuning-llms-a-comprehensive-tutorial.\n A hands-on guide to fine-tuning large language models, covering SFT, DPO, RLHF, and a full Python training pipeline. \n Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.\n You can also check exclusive content about #llm-fine-tuning-tutorial, #supervised-fine-tuning-sft, #qwen-llm-fine-tuning, #llm-training-pipeline, #hugging-face-transformers, #fine-tuning-lora, #preference-optimization-dpo, #good-company, and more.\n \n \n This story was written by: @oxylabs. Learn more about this writer by checking @oxylabs's about page,\n and for more stories, please visit hackernoon.com.\n \n \n \n Training an LLM from scratch is expensive and usually unnecessary. This hands-on tutorial shows how to fine-tune pre-trained models using SFT, DPO, and RLHF, with a full Python pipeline built on Hugging Face Transformers. Learn how to prepare data, tune hyperparameters, avoid overfitting, and turn base models into production-ready specialists.\n \n ","thumbnail_url":"https://img.transistorcdn.com/HZ9CRzf5js9DK86xzUVMWBRbXYwg4dA8xVXJGVzpL6Y/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8xMTNl/MjgwMmI0ZmEzNThj/YmJiOWNiN2UyZmRm/MzY3My5qcGVn.webp","thumbnail_width":300,"thumbnail_height":300}