LLM Fine-Tuning, LoRA & Model Customization Career Guidance in Jaipur

LLM Fine-Tuning, LoRA & Model Customization

Learn dataset preparation, supervised fine-tuning, LoRA, PEFT, quantization and model evaluation. The learning path is designed around practical understanding, guided exercises and portfolio-ready projects for students exploring modern technology careers.

Core skills to build

• Tokenization and dataset preparation
• Instruction datasets
• Supervised fine-tuning concepts
• LoRA and PEFT
• Quantization basics
• Evaluation and inference

Practical project ideas

• Fine-tune a small text classifier
• Customize a small instruction model
• Compare RAG and fine-tuning approaches
• Evaluate before-and-after model behavior

Suggested learning roadmap

Start with the required programming, data or infrastructure foundations, then move into the core tools and workflows above. Build small projects first, document what you learn, and gradually combine the skills into an end-to-end project that can be explained in an interview or portfolio.

Frequently asked questions

Is this suitable for beginners?

Students should first strengthen the prerequisite foundations, then progress step by step into the specialised workflow.

Should I focus on tools or concepts?

Both matter. Tools change over time, so practical projects should also explain the underlying concepts and trade-offs.

What should a portfolio include?

Include at least one working project, a clear README, screenshots or results, and a short explanation of the problem, approach and outcome.