LLM Fine-Tuning – Practical, Task-Based Course

Material Includes

• Daily Jupyter notebooks with step-by-step code for each task
• Training templates for HuggingFace Trainer, Seq2SeqTrainer, and LoRA workflows
• Pre-configured environment setup guide (transformers, accelerate, wandb, etc.)
• Model deployment templates using Gradio and FastAPI
• Publishing guide for HuggingFace Hub & Spaces
• LoRA + PEFT cheat sheet for efficient fine-tuning techniques

Requirements

• A machine with GPU access (Google Colab Pro, Kaggle, or local CUDA setup recommended)
• Proficiency in Python, familiarity with transformers and HuggingFace tools
• A HuggingFace account (for uploading models and using Spaces)
• Install required libraries: transformers, datasets, accelerate, peft, bitsandbytes, wandb, gradio, fastapi
• Keep a GitHub or HF repo ready to track and publish your daily work
• Join each session live or follow the structured daily task flow at your own pace

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Audience

• Developers with Python experience looking to dive into LLM fine-tuning
• Machine Learning Engineers who want to move beyond pre-trained models
• Data Scientists working with NLP pipelines or exploring generative AI
• AI researchers & enthusiasts wanting hands-on practice with HuggingFace tools
• Professionals familiar with Transformers, tokenization, and PyTorch/TensorFlow basics
• Anyone aiming to build, fine-tune, and deploy custom language model applications