Complete Guide: Fine-Tuning DeepSeek R1 LLM (Step-by-Step)

In this video, I'll walk you through the complete setup and fine-tuning process for the DeepSeek R1 large language model — from environment setup to cloud GPU usage, training, and inference! GitHub: https://github.com/codewithaarohi/Fine-tune-LLMs Cloud GPU Service - https://www.thundercompute.com/ For collaborations, sponsorships, or other inquiries, feel free to contact me at: aarohisingla1987@gmail.com 🔍 What You’ll Learn: 1- What is DeepSeek-R1 and its different model sizes (1.5B to 671B). 2- Hardware requirements for training DeepSeek-R1 (GPU, CPU, RAM, Storage). 3- How to choose an affordable cloud GPU provider (why I chose ThunderCompute ). 4- Step-by-step guide to creating a remote GPU instance. 5- Setting up your Python virtual environment and installing required deep learning libraries. 6- Downloading the DeepSeek-R1 7B model from Hugging Face. 7- Installing and using PEFT and LoRA for efficient fine-tuning. 8- Uploading and running training scripts on remote servers. 8- Downloading your trained model locally for inference.