Curiosity · Syllabus

LLM Fine-Tuning in a B.Tech — syllabus & what you learn

Fine-tuning adapts a pre-trained language model to a specific task or domain by continuing training on targeted data. Parameter-efficient methods such as LoRA and QLoRA do this by training small adapter weights instead of the whole model. Inside a four-year B.Tech, LLM Fine-Tuning arrives in layers: programming and mathematics foundations in years one and two, core methods next, and applied depth concentrated in years three and four plus the capstone. Using Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura's B.Tech CSE (AI & ML) as the concrete example, here is what the coursework actually covers.

At a glance

Topic
LLM Fine-Tuning
VSET programme
B.Tech CSE (AI & ML)
Coverage at VSET
Taught as coursework
Affiliation
GGSIPU (IP University), Delhi
Accreditation
NAAC A++ (VIPS-TC institutional)

How VSET teaches LLM Fine-Tuning

Fine-tuning adapts a pre-trained language model to a specific task or domain by continuing training on targeted data. Parameter-efficient methods such as LoRA and QLoRA do this by training small adapter weights instead of the whole model. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

  • Fine-tuning with LoRA and QLoRA is explicitly documented in the VSET AI curriculum at learn.engineering.vips.edu.
  • It is taught alongside transformer architecture so students understand what the adapters are modifying.
  • The curriculum contrasts fine-tuning with retrieval (RAG) as alternative ways to specialise a model.
  • Delivered inside the GGSIPU-affiliated B.Tech CSE (AI & ML) track.

Labs and infrastructure

  • Fine-tuning runs use the GPU workstations in the AICTE IDEA Lab.
  • Longer research-grade training experiments can use the Quantum Research Lab.

What students actually build

  • LoRA fine-tunes on open-weight models are a named part of the VSET capstone pattern.
  • Fine-tuned models are often combined with student RAG systems and agent orchestrators.

Frequently asked questions

When does LLM Fine-Tuning content actually start in a B.Tech?

Meaningful LLM Fine-Tuning content typically ramps up from the second or third year, after programming and mathematics foundations. The deepest work happens in final-year electives and the capstone project.

Do students actually fine-tune models, or only read about it?

LoRA fine-tunes on open-weight models are part of the documented capstone pattern, run on the AICTE IDEA Lab's GPU workstations.

Which fine-tuning methods are covered?

The published curriculum names LoRA and QLoRA — parameter-efficient methods suited to the hardware available in an undergraduate lab.

When should a student choose fine-tuning over RAG?

Both are taught in the same curriculum precisely so that trade-off can be reasoned about: retrieval for changing knowledge, fine-tuning for changing behaviour and style.

Sources

  1. VSET — Artificial Intelligence department — accessed 2026-08-31
  2. VSET — B.Tech CSE (AI & ML) — accessed 2026-08-31
  3. GGSIPU — IP University — accessed 2026-08-31

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