Fine-Tuning LLaMA from Scratch with PyTorch (No Trainer) (w/Python Code)

#finetuning #llm#python 📌 GitHub Code: https://github.com/mohan696matlab/LLAMA_Finetuning_Master_Class/blob/main/introduction_to_finetuning.ipynb 📌 1:1 AI Consulting: https://topmate.io/balyogi_mohan_dash_phd/ 📌 freelance profile: https://www.upwork.com/freelancers/~012513f43380c0b7b7?mp_source=share ============================= LLM Playlist: https://www.youtube.com/playlist?list=PLoSULBSCtofc-sUb3o3ZbGB8s83HqRxbs GenAI Project Playlist: https://www.youtube.com/playlist?list=PLoSULBSCtofdnEFRNYbpLtlNb4AGRqjqc Whisper ASR Playlist: https://www.youtube.com/playlist?list=PLoSULBSCtofdtLBwnooRPc-c_SuRAWTwj ============================= In this tutorial, we take a deep dive into fine-tuning a large language model (LLM)—specifically Meta’s LLaMA 1B model—completely from scratch, without using high-level tools like Hugging Face’s Trainer or Unsloth. If you’ve ever wanted to learn how to manually fine-tune a language model using pure PyTorch, this video is for you. 🚀 What You'll Learn: How to load the LLaMA 1B model directly from Hugging Face with just 2300 MB of VRAM. Understand the text generation mechanism of LLaMA models. Build custom utility functions to monitor GPU memory usage and manage cache cleanup. Use AutoModelForCausalLM, tokenizers, and PyTorch DataLoader to feed data efficiently. Generate responses using basic prompts and observe how LLaMA handles inference. Fine-tune the model by tokenizing a custom dataset, computing the loss, and updating weights with the Adam optimizer. Monitor training progress with real-time loss and GPU usage tracking. Evaluate the fine-tuned model by asking questions and interpreting generated responses. 🧠 Whether you're a researcher, developer, or ML enthusiast, this hands-on guide gives you full control over the LLM fine-tuning pipeline, helping you build a deep understanding of how it all works under the hood. 📌 Coming Soon: In future videos, we’ll explore advanced LLM fine-tuning techniques like: Quantization-aware training LoRA (Low-Rank Adaptation) Memory-efficient fine-tuning under 2-3GB VRAM 🔥 Don’t forget to LIKE, SUBSCRIBE ============================= 🔗Links🔗 LinkedIn: https://www.linkedin.com/in/balyogi-mohan-dash/ Google Scholar: https://scholar.google.com/citations?user=jzcIElIAAAAJ&hl=en Automatic Transcription Video: https://youtu.be/MkuttKl5wBk ============================= Please reach out via email for any questions: [email protected] TIMESTAMPS 00:00 Introduction to fine-tuning large language models and upcoming optimization techniques. 01:25 Importing the model and setting important configurations 07:25 Demonstrating the model's tendency to repeat tokens without proper handling. 08:44 Defining labels and logits for calculating cross-entropy loss during training. 11:03 Illustrating how to get token predictions by taking the arg max of the probability distribution. 14:43 Presenting the true answers to a NEW questions 15:45 Showing Llama model's initial inability to answer the NEW question 17:49 Describing the iterative text generation process of LLM 20:42 Observing the decreasing loss during training 22:43 Summarizing the video's content on fine-tuning Lama