Fine-Tune Your Own LLM (Free GPU, Step by Step)
In production, premiere planned 6 March 2027 BeginnerIntermediateAdvanced
Get notified on YouTube Udemy certificate: coming soon
- Udemy
- Coming soon
- Lessons
- 11
- Planned length
- About 3 h 10 min
- Projects
- 1
- Practicals
- Planned: a hands-on practical and a Udemy assignment in every section, plus a capstone
What you will be able to do
- Decide for three real work scenarios
- Read a loss curve and explain it
- Collect, clean and split a dataset without leakage
- Know why LoRA fits on one GPU
- Train a LoRA adapter
- Fine-tune a 4-bit model on a free GPU
Syllabus
Training LLMs: How Training Works
- Fine-Tune vs Prompt vs RAG Decide for three real work scenarios
- How LLM Training Works: Tokens, Loss, Pre-training vs Fine-tuning Read a loss curve and explain it
Training LLMs: Training Data
- Build a 300-1,000 Example Dataset: Clean, Split, Licence-Check Collect, clean and split a dataset without leakage
Training LLMs: Fine-Tuning Methods
- Full Fine-Tuning vs Parameter-Efficient (PEFT) Know why LoRA fits on one GPU
- LoRA Explained and Applied Train a LoRA adapter
- QLoRA: Bigger Models on Small GPUs Fine-tune a 4-bit model on a free GPU
- Your First Fine-Tune on Colab (Instruction Tuning) Instruction-tune a model and resume after disconnect
- Make It Better: Data, Hyperparameters, Continued Pre-training, Multi-GPU Run a second training that beats the first
Training LLMs: Evaluate, Compress and Ship
- Evaluate Your Fine-Tune Properly Produce a before/after table on a held-out set
- Merge, Export, Run Your Model (and Model Merging) Run your fine-tune in Ollama behind an API
- Capstone: Fine-Tune for a Real Work Task Deliver a model, eval report and demo
Take before
Lesson list from the ADITEJUS curriculum. Planned lengths are estimates per lesson; Udemy shows the final length.