Hands-On Fine-Tuning (LoRA/QLoRA)
Complete end-to-end labs on BERT/LLaMA/GPT with parameter-efficient training, dataset curation, and reproducible reports.
Large Language Model Engineering Course — Advanced Training
Sharpen your LLM skills end-to-end: master the Transformer architecture, modern attention variants (GQA, MLA), and efficient fine-tuning (SFT, LoRA/QLoRA). Build retrieval-aware, evaluation-driven applications using DeepSeek, Mistral, Llama 3, and Qwen—without diving into infra or LLMOps.
- Understand tokens, embeddings, context windows & sampling.
- Compare open models (DeepSeek, Mistral, Llama 3, Qwen) for your task.
- Fine-tune with clean data pipelines & overfitting controls.
- Build RAG & memory patterns with grounded answers and citations.
- Evaluate quality & safety with practical test harnesses.
Designed for engineers and builders who want to go beyond prompts and actually shape model behavior.
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Skills You Will Gain in Large Language Models Course
From Transformer foundations to LoRA/QLoRA fine-tuning, RLHF alignment, and LLM deployment — master the end-to-end Large Language Model pipeline.
Foundation (Transformers & NLP)
TransformersAttentionEmbeddingsTokenizationBERT vs GPT
Fine-Tuning & Optimization
Fine-TuningLoRAQLoRAHyperparameter TuningDistributed Training
Alignment & Evaluation
RLHF / DPOBias MitigationHallucination ReductionBLEU/ROUGE/Perplexity
LLM Engineering
Prompting & PatternsLangChainRAG SystemsExperiment Tracking
Deployment & CI/CD
Model ServingvLLM / TGICI/CDMonitoring & TracingCost Optimization
Additional Focus Areas
Text GenerationDataset CurationSafety & GuardrailsMonitoring & TracingModel Cards & Reporting
Program Highlights — LLM Engineering That Gets You Hired
From Transformer fundamentals to LoRA/QLoRA fine-tuning, RLHF/DPO, RAG with LangChain, and production deployment with vLLM/TGI.
RLHF / DPO & Evaluation
Learn alignment methods, preference data pipelines, and quality measurement (BLEU, ROUGE, Perplexity) to reduce hallucinations.
Capstone: Build Your Own GPT
Train, evaluate, and deploy a GPT-style model on domain data. Publish a model card and demo—perfect for interviews.
Production LLMOps & Career Support
Serve with vLLM/TGI, add tracing (LangSmith), cost controls, and CI/CD. Get certification, resume help, and mock interviews.
Program Overview of Large Language Model Course
Generative AI with Large Language Models is making big waves in the tech world. This course is designed to give you a solid grasp of how these models work and how you can use them in real-world applications.
We'll start with the basics, covering what LLMs are, how they're built, and what makes them tick. You'll learn about neural networks, deep learning, and natural language processing in a straightforward way that makes these complex topics easier to understand.
The course isn't just about theory. You'll get plenty of hands-on practice with exercises that help you build and deploy your own generative AI projects. Real-life examples and case studies will show you how LLMs are used in different industries, like creating content and improving customer service.
We’ll also dive into more advanced topics, like fine-tuning models and optimizing their performance. Plus, we'll discuss the important ethical questions around AI, making sure you're ready to create AI solutions that are both effective and responsible.
Who Is This Program For?
For India & global talent aiming at LLM engineering roles—from fine-tuning (LoRA/QLoRA) and RLHF/DPO to RAG systems, deployment (vLLM/TGI), and evaluation with guardrails.
LLM / ML Engineers
End-to-end LLM engineering: Transformers, LoRA/QLoRA fine-tuning, RLHF/DPO, evaluation, and production deployment with tracing.
Software Developers
Go beyond API calls—fine-tune BERT/GPT, integrate RAG with LangChain, and ship reliable AI features to users.
Research / Robotics Engineers
Design datasets, run controlled experiments, and publish model cards; study MoE trade-offs, safety, and hallucination reduction.
IoT & Edge Engineers
Build on-device assistants for diagnostics and control; optimize token budgets and latency for edge constraints.
Security & Cyber Ops
Incident summarization, anomaly narratives, redaction/PII guardrails, and safe-prompting patterns with measurable risk controls.
Founders & Product Builders
Prototype domain copilots and chatbots fast; use PEFT to cut costs, then scale serving with vLLM/TGI and autoscaling.
Product Managers & Leaders
Scope AI features responsibly—define metrics, eval loops, and success criteria for trustworthy LLM user experiences.
Data Engineers
Pipelines for fine-tuning datasets, embeddings, vector DBs, retrieval/re-ranking, and experiment tracking at scale.
IT / System Architects
Integrate auth, observability (LangSmith), rate limits, and SLAs; manage cost per token and throughput budgets.
Networking & Telecom
Ops copilots for ticket triage and knowledge search; evaluate latency/quality trade-offs under real traffic.
Students & Aspiring DS
Foundations → tokenization/attention → PEFT → RLHF/DPO → RAG → deploy & evaluate, with portfolio projects.
Learning Outcomes for Large Language Model Course
Graduate with LLM engineering skills across Transformer theory, LoRA/QLoRA fine-tuning, RLHF/DPO alignment, RAG systems, and production deployment.
1. Master Transformer Fundamentals
Understand attention, multi-head attention, embeddings, and positional encodings to read and reason about modern LLM internals (BERT, GPT, LLaMA, Mistral).
TransformersAttentionEmbeddingsBERT vs GPT
2. Fine-Tune Efficiently with LoRA / QLoRA
Perform parameter-efficient fine-tuning on domain data, run hyperparameter sweeps, and produce reproducible reports and model cards.
LoRAQLoRAPEFTHyperparameter Tuning
3. Align Models with RLHF / DPO
Build preference datasets and align models with RLHF and DPO to improve helpfulness, safety, and task adherence; evaluate and reduce hallucinations.
RLHFDPOSafetyHallucination Reduction
4. Scale with Mixture-of-Experts (MoE)
Learn MoE routing and expert activation to increase capacity without linear cost; reason about trade-offs in throughput, latency, and quality.
MoERoutingLatencyThroughput
5. Build RAG & LLM Apps (LangChain / LlamaIndex)
Design retrieval pipelines, choose embeddings/vector stores, and implement evaluation loops for enterprise-grade RAG chatbots and copilots.
RAGLangChainLlamaIndexVector DB
6. Deploy & Observe (vLLM / TGI + Tracing)
Serve models with vLLM/TGI, add tracing and observability (LangSmith), enforce guardrails, and manage cost per token and throughput SLAs.
vLLMTGIObservabilityGuardrails
7. Train & Scale in Production
Use distributed training on GPUs/TPUs, optimize memory/throughput, and apply cost-aware strategies for sustainable LLM operations.
Distributed TrainingGPUs/TPUsCost Optimization
Why Learn Large Language Models Now?
LLMs are redefining software. Upskill from Transformer fundamentals to LoRA/QLoRA fine-tuning, RLHF alignment, and evaluation to stay ahead.
6×
Global GenAI Learning Growth
YoY acceleration
107%
India LLM Enrollments
YoY increase
54%
Higher Pay with AI Skills
Employer premium
Top 5
Role: LLM Engineer
Rising AI job family
Real Industry Impact
Build copilots, RAG systems, and domain chatbots. Learn what companies hire for now.
Hands-On, Not Just Theory
Fine-tune BERT/GPT with LoRA/QLoRA, practice RLHF/DPO, and measure with BLEU/ROUGE/Perplexity.
Career Upside & Portability
Globally useful skills. Graduate with a capstone GPT-style model, eval report, and certification.
Large Language Model Course Curriculum
A step-by-step path from transformer basics to real apps with retrieval, memory, and evaluation. Learn to adapt models to your data, ship reliable APIs, and monitor quality in production.
What Sets Our LLM Mastery Course Apart?
Compare how our Large Language Model (LLM) training delivers real-world fine-tuning, RLHF, and AI engineering skills versus generic online courses.
| Feature | Our LLM Mastery Program | Other AI Courses |
|---|---|---|
| LLM Engineering Depth | ✔ Covers Transformers, BERT, GPT, LoRA/QLoRA fine-tuning, RLHF, and model alignment. | ✘ Limited to prompt writing or API usage without true engineering depth. |
| Hands-On Fine-Tuning Projects | ✔ Train real models with LoRA/QLoRA using BERT, LLaMA, and GPT architectures. | ✘ Mostly theoretical examples or copied notebooks. |
| RLHF & Alignment Modules | ✔ Learn RLHF, DPO, and safety evaluation with simulated feedback data. | ✘ Skip human-feedback training entirely. |
| Toolchain & Frameworks | ✔ Hugging Face, PyTorch, PEFT, LangChain, Gradio, LangSmith — real production stack. | ✘ Use basic OpenAI APIs with no infrastructure exposure. |
| Capstone: Build Your Own GPT | ✔ Final project: fine-tune and deploy a GPT-style model end-to-end with evaluation. | ✘ No complete hands-on project. |
| GPU-Optimized Learning | ✔ Works seamlessly with Colab GPUs or budget hardware using LoRA/QLoRA. | ✘ Requires high-end GPUs or cloud credits. |
| Career-Ready Certification | ✔ Get certified by School of Core AI with portfolio-ready projects and interview prep. | ✘ Certificates only — no real project validation. |
| Constant Curriculum Updates | ✔ Updated every 2 months with the latest LLM architectures and open-source tools. | ✘ Outdated modules, rarely refreshed. |
Your LLM Learning Curve
Move from Data Science fundamentals → Generative AI fine-tuning → production-grade LLMOps. A complete journey to become an LLM Engineer.
FoundationsStep 1/3
Stage 1 · Build Strong AI Foundations
Master Python, statistics, and core ML. These fundamentals make LLM training dynamics intuitive.
Explore Data Science Foundations
Data Science Course
Fine-TuningStep 2/3
Stage 2 · Master GenAI & LLM Fine-Tuning
Hands-on with Transformers, Prompting, and LoRA/QLoRA fine-tuning. Align and evaluate real models.
Advance to GenAI Specialization
Generative AI Course
DeploymentStep 3/3
Stage 3 · Deploy, Scale & Monitor LLMs
Ship production systems: inference optimization, vLLM/TGI serving, tracing, and cost control.
Master LLM Deployment
LLMOps Course
Large Language Model Course Fee Structure
Competitive pricing from one of the best LLM training institutes in Gurgaon. Transparent, no hidden fees.
One-Time Payment (Best Value)
- LLM engineering essentials: Transformers, tokenization, embeddings, attention.
- Fine-tuning (LoRA/QLoRA): domain datasets, hyperparameter sweeps, model cards.
- Alignment (RLHF/DPO) & evaluation: preference data, BLEU/ROUGE/Perplexity, hallucination reduction.
- RAG systems: LangChain/LlamaIndex, embeddings & vector DBs (Qdrant/Faiss), re-ranking, citations.
- Deployment: vLLM / TGI serving, LangSmith tracing, guardrails, cost per token dashboards.
- Multimodal preview: vision–language concepts for GenAI (images → text, captioning, VQA).
Included Benefits
- Dual certification • Classroom or live virtual (same fee)
- Career assistance: resume/GitHub review, mock interviews, referrals where available
- No additional platform charges
Total
₹35,000
+ Taxes (if applicable)
Bundle & Save: add LLMOps or Generative AI course for deeper coverage (deployment, cost control, vision–language multimodal) — ask about current bundle discounts.
Testimonials of our Successful Learners
What our learners have to say
"Taking the Large Language Model Course was a turning point in my career. I started with a basic understanding of AI, but the course helped me dive deep into LLMs and their applications. I applied this knowledge to create a customer service chatbot, improving efficiency within my team. The hands-on lessons in model fine-tuning and optimization gave me the practical skills I needed to transition smoothly into this field."
Arjun Mehta
"This course was perfect for me as I wanted to dive deeper into Generative AI. The Large Language Model Course helped me understand how LLMs can be applied to real-world problems, such as creating recommendation systems and advanced search engines. The course provided clear, practical guidance, and I now feel confident working with LLMs on various projects."
Priya Sharma
"The Large Language Model Course provided the right balance of theory and practice. It helped me work on real-world applications, such as summarizing documents and answering complex questions using LLMs. The fine-tuning techniques we learned allowed me to optimize models for specific tasks. The hands-on approach made it much easier to apply these skills in my daily work."
Rahul Kapoor
"Switching to ML engineering was a major career shift for me, and the Large Language Model Course made it easy. The course gave me the skills to develop a text summarization tool for our team, helping us process information faster. The hands-on exercises with model fine-tuning and quantization allowed me to optimize the tool for real-world performance."
Vikram Choudhury
"The Large Language Model Course was exactly what I needed to integrate LLMs into real-world applications. I used what I learned to develop systems for document classification and data extraction, helping my team streamline information flow. The course provided practical insights into how to deploy and scale LLMs, which directly impacted the solutions I was building."
Ananya Desai
"I took the Large Language Model Course to build more effective AI systems for my team. I learned how to apply LLMs to develop a customer support system that could handle more complex queries. The course’s practical focus gave me the tools to fine-tune models and implement them in real-world scenarios, which has improved our efficiency and response times."
Nandini Reddy
Frequently Asked Questions About the Large Language Model Course
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