In this video, we show you how to fine-tune DeepSeek R1, an open-source reasoning model, using LoRA (Low-Rank Adaptation). We'll also be using Kaggle, Hugging Face and Weights & Biases. We walk you through data preparation, model configuration, and optimization, including advanced techniques like four-bit quantization for efficient training on consumer GPUs.
By the end of this tutorial, you’ll be equipped with the skills to customize DeepSeek R1 for your own specialized tasks, such as medical reasoning.
🔗 Resources & Tutorials
Kaggle Notebook: https://www.kaggle.com/code/aan1994/fine-tuning-deepseek-r1-reasoning-model-youtube
How Transformers Work: https://www.datacamp.com/tutorial/how-transformers-work
Fine-Tuning DeepSeek R1 Reasoning Model: https://www.datacamp.com/tutorial/fine-tuning-deepseek-r1-reasoning-model
DeepSeek R1 Blog Overview: https://www.datacamp.com/blog/deepseek-r1
Understanding Janus Pro: https://www.datacamp.com/blog/janus-pro
DeepSeek R1 Project Walkthrough: https://www.datacamp.com/tutorial/deepseek-r1-project
DeepSeek vs ChatGPT: https://www.datacamp.com/blog/deepseek-vs-chatgpt
Qwen-2.5 MAX Model: https://www.datacamp.com/blog/qwen-2-5-max
DeepSeek R1 Ollama Tutorial: https://www.datacamp.com/tutorial/deepseek-r1-ollama
📕 Chapters
00:00 Introduction
00:30 Why Fine-Tuning DeepSeek Matters
02:30 LoRA Explained with a PS5 Factory Analogy
05:20 Tools & Setup Overview
09:00 Loading DeepSeek R1 Model and Tokenizer
16:10 Formatting Data for Fine-Tuning
23:00 Applying LoRA for Efficient Updates
34:00 Configuring Training Parameters
43:15 Running the Fine-Tuning Process on Kaggle
46:00 Comparing Model Performance After Fine-Tuning
47:50 Final Thoughts on Future Models
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