Curiosity · Course availability

Does GGSIPU have a course in LLM Fine-Tuning?

Not as a standalone degree title, but yes as real coursework: VSET (VIPS-TC) covers LLM Fine-Tuning inside B.Tech CSE (AI & ML). 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. Below is what that coverage actually includes and what to verify before counting on it.

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.

How admission works

Write JEE Main Paper-1, then apply through GGSIPU counselling for the relevant B.Tech programme at VSET. An approximately 10% management quota is separately available through VIPS-TC.

Frequently asked questions

Does GGSIPU have a course in LLM Fine-Tuning?

Not as a standalone degree title, but yes as real coursework: VSET (VIPS-TC) covers LLM Fine-Tuning inside B.Tech CSE (AI & ML).

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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