Advanced LLM Engineering • Live Online Cohort
Large Language Model Course for Engineers
Fine-tune, align and evaluate open-source LLMs—not just call APIs.
Already comfortable with Python, statistics, machine learning and neural networks? Go inside the model. Across eight live weeks, learn Transformer internals, training-data preparation, supervised fine-tuning, LoRA/QLoRA, preference alignment with DPO and RLHF concepts, model evaluation and release decision-making.
Fine-Tuned Adapter · Evaluation Harness · Model Card · Capstone
Confirm your prerequisites, batch timing and course fit before enrolling.
LLM Model Lifecycle
InteractiveMODEL FOUNDATION
Understand tokenization, embeddings, attention and Transformer blocks.
8 Weeks
Duration
Live + Recordings
Delivery
4–5 Hours/Week
Commitment
₹30,000 One-Time
Fee
Labs + Capstone
Assessment
Dual Certificate
Credential
What Is a Large Language Model Course?
A Large Language Model course teaches engineers how Transformer-based models represent language, learn patterns from data, adapt to specialised tasks and are evaluated before release. This advanced program focuses on the model-engineering layer: tokenization, attention, supervised fine-tuning, parameter-efficient adaptation, preference alignment, evaluation and model documentation.
This program focuses on understanding and deliberately changing model behaviour — not prompt libraries, API wrappers, or serving infrastructure.
Level
Advanced
Primary focus
LLM model engineering
Starting point
Python, ML and neural-network knowledge
Core outcome
Fine-tune, align, evaluate and document an open-source LLM
Who Should Take This Advanced Large Language Model Course?
This course is designed for
- ML engineers moving into LLM engineering
- NLP engineers working with Transformer-based systems
- Data scientists who understand neural networks
- Software engineers with a practical machine-learning foundation
- Generative AI developers ready to move beyond prompting and APIs
You should already be comfortable with
- Writing Python for data and machine-learning workflows
- Probability, statistics and standard ML concepts
- Neural networks, loss functions and training loops
- Reading model metrics and debugging experiments
- Using notebooks or Python scripts for technical work
This is not a beginner Python, data science or prompt-engineering course. If you do not yet meet the prerequisites, start with the appropriate foundation or Generative AI program.
The value is not access to another notebook. It is learning how to make and defend model-engineering decisions using evidence.
What You Will Learn: Large Language Model Architecture, Fine-Tuning and Alignment
The course follows the same connected decisions used in real model adaptation: understand the architecture, prepare data, establish a baseline, fine-tune efficiently, align behaviour, evaluate failures and document evidence for a release decision.
Stage 1 of 6
Transformer Architecture, Tokenization and Attention
Study the decoder-only Transformer architecture that powers modern LLMs — from tokenization through attention to output generation.
- Tokenization, vocabulary and subword encoding
- Embeddings and positional information
- Self-attention and multi-head attention mechanics
- Decoder-only blocks, layer normalisation and residual connections
- How architectural choices affect context handling, memory use and output behaviour
Transformer Architecture, Tokenization and Attention
Study the decoder-only Transformer architecture that powers modern LLMs — from tokenization through attention to output generation.
- Tokenization, vocabulary and subword encoding
- Embeddings and positional information
- Self-attention and multi-head attention mechanics
- Decoder-only blocks, layer normalisation and residual connections
- How architectural choices affect context handling, memory use and output behaviour
How Large Language Models Are Trained
Understand the pretraining pipeline — objectives, data scale, compute trade-offs and model families. The course explains pretraining conceptually; you will not pretrain a frontier-scale model.
- Next-token prediction and training objectives
- Data scale, token budgets and compute trade-offs
- Encoder, encoder-decoder and decoder-only model families
- Scaling laws and when pretraining stops being practical
- What the course covers vs. what it explains conceptually
Supervised Fine-Tuning for Large Language Models
Prepare instruction data, establish a baseline and adapt a pretrained model to a defined task with reproducible training.
- Instruction data preparation and formatting
- Quality filtering and train-validation-test separation
- Establishing a reproducible baseline checkpoint
- Full-parameter vs. partial fine-tuning decisions
- Tracking metrics, loss curves and overfitting signals
LoRA and QLoRA Fine-Tuning
Use parameter-efficient fine-tuning to adapt model behaviour under realistic compute limits, with clear control over the quality-versus-memory trade-off.
- PEFT principles: why adapters work
- LoRA rank, scaling and target module selection
- QLoRA: 4-bit quantization + LoRA for low-VRAM training
- Adapter merging, checkpointing and reproducibility
- Quality-versus-memory trade-off and when to choose full SFT
Large Language Model Alignment with DPO and RLHF
Study preference data, DPO and the role of RLHF in post-training alignment — including where alignment improves one property while weakening another.
- Preference data collection and formatting
- Direct Preference Optimization (DPO) training loop
- Reward modelling and RLHF overview
- Comparing model behaviour before and after alignment
- Trade-offs: helpfulness vs. safety, verbosity vs. conciseness
LLM Evaluation, Quantization and Model Cards
Build a task-specific evaluation set, inspect failure modes, compare the adapted model against its baseline and document release evidence in a model card.
- Task-specific evaluation set design
- Failure mode inspection and categorisation
- Comparing adapted model vs. baseline on metrics
- Quantization fundamentals for deployment readiness
- Model cards: quality, safety, limitations and release recommendations
Large Language Model Projects You Will Build
The practical work is organised as controlled model experiments. Each project asks an engineering question, performs defined work, produces reviewable evidence and demonstrates a specific decision. The capstone combines all four into one assessed release package.
Transformer Mechanics Report
Engineering question
How do tokenization, attention and architectural choices shape model output?
Work performed
Inspect tokenization, embeddings, attention behaviour, positional information and context limitations across a learning-scale decoder-only Transformer.
Evidence produced
Architecture notebook, attention inspection logs and written analysis connecting architectural choices to model quality, memory use and inference behaviour.
Decision demonstrated
Demonstrates the ability to reason about model internals rather than treat the LLM as a black box.
LoRA/QLoRA Fine-Tuning Experiment
Engineering question
Does parameter-efficient fine-tuning improve task performance over the baseline model?
Work performed
Prepare a domain dataset, establish a baseline, run supervised fine-tuning with PEFT and compare the adapted model against the original across quality and resource metrics.
Evidence produced
Trained adapter, experiment log, baseline-versus-adapted evaluation and comparison of adapter configurations.
Decision demonstrated
Demonstrates the ability to choose and justify an adaptation method under compute constraints.
Preference Alignment Experiment
Engineering question
Does preference alignment improve task adherence without damaging useful capabilities?
Work performed
Create or inspect preference data, apply DPO-style alignment and compare model behaviour before and after. Understand where RLHF fits and when alignment can harm useful skills.
Evidence produced
Alignment comparison report, preference-pair documentation and failure analysis showing where alignment helps and where it regresses.
Decision demonstrated
Demonstrates the ability to evaluate alignment trade-offs rather than assume alignment always improves models.
LLM Evaluation and Release Gate
Engineering question
Is the adapted model reliable enough for the intended use case?
Work performed
Build an evaluation set covering task quality, instruction following, safety and important failure cases. Use the results to make a clear release, revise or reject decision.
Evidence produced
Evaluation harness, release scorecard, failure taxonomy and model card documenting quality, safety and limitations.
Decision demonstrated
Demonstrates the ability to make and defend a release decision using evidence rather than intuition.
Capstone: Adapt and Evaluate an Open-Source LLM
Engineering question
Can you justify model selection, adaptation strategy, evaluation and release recommendation as one coherent engineering package?
Work performed
Select a defined domain problem, adapt an open-source language model, compare it with a baseline and present the evidence behind your engineering decisions.
Evidence produced
Complete capstone release package: dataset report, experiment configuration, baseline comparison, preference evaluation, failure analysis, model card and inference demonstration.
Decision demonstrated
Demonstrates end-to-end model-engineering judgement across the full lifecycle from selection to release recommendation.
8-Week Large Language Model Course Curriculum
The 8-week structure moves from model understanding to an assessed adaptation and evaluation release. The public curriculum shows learning outcomes and engineering stages; detailed lab instructions, datasets, code walkthroughs and solution reviews remain inside the enrolled program.
The public curriculum shows learning outcomes and engineering stages. Detailed lab instructions, datasets, code walkthroughs and solution reviews remain inside the enrolled program.
Week 1: Tokens, Embeddings and LLM Foundations
Trace text from tokenization to embeddings and model input. Compare encoder, encoder-decoder and decoder-only model families and understand why modern generative LLMs commonly use autoregressive decoder architectures.
Trace text from tokenization to embeddings and model input. Compare encoder, encoder-decoder and decoder-only model families and understand why modern generative LLMs commonly use autoregressive decoder architectures.
Week 2: Inside the Decoder-Only Transformer
Inspect self-attention, causal masking, residual connections, normalization, positional strategies and feed-forward layers. Reason about MHA, MQA and GQA trade-offs at an architectural level.
Inspect self-attention, causal masking, residual connections, normalization, positional strategies and feed-forward layers. Reason about MHA, MQA and GQA trade-offs at an architectural level.
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Week 3: Training Data and Supervised Fine-Tuning
Define a task, establish a baseline and prepare instruction data. Work through formatting, cleaning, train-validation separation, loss behaviour and experiment reproducibility.
Define a task, establish a baseline and prepare instruction data. Work through formatting, cleaning, train-validation separation, loss behaviour and experiment reproducibility.
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Week 4: LoRA, QLoRA and Efficient Adaptation
Fine-tune an open-source model using parameter-efficient techniques. Compare adapter configurations, memory requirements, training behaviour and output quality.
Fine-tune an open-source model using parameter-efficient techniques. Compare adapter configurations, memory requirements, training behaviour and output quality.
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Week 5: Preference Data, DPO and RLHF
Understand preference-pair construction, reward modelling concepts, DPO and the wider RLHF pipeline. Compare model behaviour before and after preference alignment.
Understand preference-pair construction, reward modelling concepts, DPO and the wider RLHF pipeline. Compare model behaviour before and after preference alignment.
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Week 6: Evaluation, Failure Analysis and Quantization
Create a task-specific evaluation set, measure quality against a baseline and inspect robustness, unsafe behaviour and important failure cases. Study quantization as a model-efficiency and release consideration.
Create a task-specific evaluation set, measure quality against a baseline and inspect robustness, unsafe behaviour and important failure cases. Study quantization as a model-efficiency and release consideration.
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Week 7: Capstone Experiment
Select a defined domain problem, justify the model and adaptation strategy, run the experiment and record the resulting evidence. Diagnose weak results rather than hiding them.
Select a defined domain problem, justify the model and adaptation strategy, run the experiment and record the resulting evidence. Diagnose weak results rather than hiding them.
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Week 8: Release Review and Model Card
Present the adapted model, evaluation evidence, limitations and release recommendation. Complete the model card and explain whether the model should be released, revised or rejected.
Present the adapted model, evaluation evidence, limitations and release recommendation. Complete the model card and explain whether the model should be released, revised or rejected.
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Toolchain and Compute
Open-Source Large Language Models, Tools and Compute
The program uses the PyTorch and Hugging Face ecosystem for architecture inspection, data preparation, fine-tuning and evaluation. Unsloth and Axolotl enable parameter-efficient fine-tuning on accessible hardware, while DeepSpeed and Ray Train cover distributed training for larger model runs. The course uses current open-weight model families — Qwen, Llama, DeepSeek, Gemma and others — following the ecosystem as it evolves.
Open-Weight Models Used for Learning
Exact models per cohort may vary based on hardware and release timing.
Fine-Tuning and Training Tools
Distributed Training and Evaluation
Compute Environment
Labs are designed around accessible cloud-GPU workflows. Unsloth and QLoRA enable fine-tuning on single-GPU setups, while DeepSpeed and Ray Train cover multi-GPU distributed training for larger model experiments. Exact environment and compute instructions are supplied for each experiment.
How Large Language Model Fine-Tuning and Evaluation Are Assessed
Assessment is based on the quality of the learner's engineering process and evidence—not only on whether a notebook executes successfully.
A strong submission explains the task, establishes a credible baseline, justifies the adaptation method, measures the result, reports limitations and makes a defensible release decision.
Assessment dimensions
Dimensions shown without percentages until official weightings are published.
Large Language Model Course vs Generative AI, RAG and LLMOps
The Large Language Model Course focuses on model architecture, adaptation, alignment and evaluation.
| Program | Primary question | Main depth | Typical output |
|---|---|---|---|
| Generative AI Course | How do I build across the GenAI application stack? | Prompting, applications, retrieval, multimodal and broad GenAI development | Multiple GenAI applications |
| Large Language Model Course | How do I understand and change model behaviour? | Transformers, SFT, LoRA/QLoRA, alignment and evaluation | Adapted and evaluated open-source model |
| RAG Course | How do I ground responses in external knowledge? | Chunking, embeddings, hybrid retrieval, re-ranking and grounded evaluation | Evaluated retrieval system |
| LLMOps Course | How do I operate LLM systems reliably in production? | Serving, observability, scaling, security and cost control | Production LLM platform |
Large Language Model Course Fees, Format and Certification
One fee of ₹30,000 covers eight weeks of live online instruction, recordings, guided model experiments, capstone assessment and the verified certification process. Plan for approximately 4–5 hours per week across live sessions, guided work and independent experimentation.
- Eight weeks of live instructor-led learning
- Session recordings for revision
- Guided model-engineering labs
- Fine-tuning, alignment and evaluation exercises
- A capstone project
- Capstone assessment
- Dual verifiable certification
- Portfolio and career support
Use the call to confirm
Use the call to confirm your technical background, upcoming cohort timing and whether this is the right course for your goal. Plan for approximately 4–5 hours each week, including live sessions, practice and capstone work.
Learner Reviews and Model-Engineering Capstone Evidence
Learner feedback and completed engineering work.
Individual evidence links appear as verified reviews become available.
Frequently Asked Questions
Is this LLM course suitable for beginners?
No. This is an advanced LLM engineering course. You should already understand Python, statistics, machine learning and basic neural networks. Beginners should first complete the relevant foundation or Generative AI program.
What prerequisites are required for this LLM course?
You should be comfortable writing Python for data and ML workflows, understand probability, statistics and standard ML concepts, and know neural-network fundamentals including loss functions, backpropagation and training loops. PyTorch experience is helpful but not mandatory.
How is this different from the Generative AI Course?
A Generative AI course provides breadth across prompting, applications, retrieval and AI product development. This LLM course provides greater model-level depth through Transformer internals, supervised fine-tuning, LoRA/QLoRA, preference alignment and evaluation.
Will I train a foundation model from scratch?
You will study the Transformer and training pipeline. Full-scale pretraining requires substantial compute, so the practical work focuses on adapting open-weight models using SFT, LoRA and QLoRA.
What will I build during the course?
You will complete a Transformer mechanics report, a LoRA/QLoRA fine-tuning experiment, a preference alignment experiment, an LLM evaluation and release gate, and a capstone that adapts and evaluates an open-source LLM. Each project produces reviewable engineering evidence.
What is the difference between LoRA and QLoRA?
LoRA adds low-rank trainable adapter matrices to selected model layers while keeping the base weights frozen. QLoRA combines LoRA with 4-bit quantization of the base model, significantly reducing memory requirements. The course compares both methods across adapter configuration, trainable parameters, memory usage and output quality.
Will I learn DPO and RLHF?
Yes. The course covers preference-pair construction, Direct Preference Optimization (DPO) as a practical alignment method, and the wider RLHF pipeline including reward modelling and policy optimization concepts. You will compare model behaviour before and after alignment.
Which open-source LLMs and tools are used?
PyTorch and the Hugging Face ecosystem: Transformers, Tokenizers, Datasets, PEFT, TRL, Unsloth, Axolotl and evaluation tools. Open-weight model families such as Qwen, Llama, DeepSeek, Gemma and Mistral. Exact models may vary by cohort.
What hardware or GPU access is required?
Labs use parameter-efficient approaches designed for accessible cloud-GPU environments such as Google Colab. A modern laptop handles theory, data preparation and evaluation. Compute instructions are provided per experiment.
Does the course include RAG and production deployment?
RAG is discussed so you know when retrieval is more appropriate than changing model weights. Deep retrieval engineering belongs in the <a href="/courses/rag-course" style="color:#04026C;text-decoration:underline">RAG Course</a>; serving, monitoring and scaling belong in the <a href="/courses/llmops-course" style="color:#04026C;text-decoration:underline">LLMOps Course</a>. This course focuses on the model layer. If you need broader GenAI foundations first, the <a href="/courses/generative-ai-course" style="color:#04026C;text-decoration:underline">Generative AI Course</a> covers that path.
What are the course fee, duration and weekly commitment?
8 weeks, live online with recordings. Fee: ₹30,000 one-time. Approximately 4–5 hours per week across live sessions, guided work and independent experimentation.
Will I receive a certificate after completing the course?
Yes — a dual certificate reflecting participation and assessed capstone work. The assessed evidence must meet the program standard.