LLM Fine-Tuning Training in Gurgaon

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LLM Fine-Tuning Course Overview

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Upcoming LLM Fine-Tuning Batches

21-09-2026
Mon-FriWeekdays Regular
08:00 AM & 10:00 AM Batches(Class 1Hr - 2Hrs) / Per Session
23-09-2026
Mon - FriWeekdays Regular
06:00 PM & 08:00 PM Batches(Class 1Hr - 2Hrs) / Per Session
25-09-2026
Sat-SunWeekend Batch
09:00 AM & 01:00 PM Batches(Class 2Hr - 4Hrs) / Per Session
Can't find a batch? Pick your own schedule

LLM Fine-Tuning Course Syllabus

Join our LLM Fine-Tuning Course! Learn LLM fine-tuning through dataset preparation, instruction tuning, supervised fine-tuning, LoRA, QLoRA, PEFT, preference optimization, evaluation, alignment, model optimization, safety, and production deployment workflows.

  • LLM fundamentals
  • Pre-training vs fine-tuning
  • Foundation models
  • Fine-tuning use cases
  • Domain adaptation
  • Fine-tuning lifecycle
  • Model selection
  • Transformer architecture
  • Attention mechanisms
  • Tokenization
  • Embeddings
  • Positional encoding
  • Encoder and decoder architectures
  • Decoder-only LLMs
  • Dataset collection
  • Data cleaning
  • Data deduplication
  • Data preprocessing
  • Instruction-response formatting
  • Conversational datasets
  • Train and validation splits
  • Data quality evaluation
  • Instruction tuning fundamentals
  • Supervised Fine-Tuning (SFT)
  • Prompt-response datasets
  • Chat model fine-tuning
  • Multi-turn conversations
  • Training configurations
  • Checkpoint management
  • PEFT fundamentals
  • LoRA
  • QLoRA
  • Adapters
  • Prefix tuning
  • Prompt tuning
  • Parameter and memory efficiency
  • Domain-specific fine-tuning
  • Task-specific customization
  • Continued pre-training
  • Multi-task fine-tuning
  • Instruction dataset design
  • Synthetic training data
  • Curriculum strategies
  • Human preference data
  • Reward modeling fundamentals
  • RLHF concepts
  • Direct Preference Optimization (DPO)
  • ORPO
  • Preference dataset preparation
  • Alignment evaluation
  • Hugging Face Transformers
  • Hugging Face Datasets
  • PEFT
  • TRL
  • Accelerate
  • Unsloth
  • PyTorch
  • Experiment tracking
  • GPU fundamentals
  • VRAM management
  • Mixed-precision training
  • FP16 and BF16
  • Gradient accumulation
  • Gradient checkpointing
  • Memory-efficient attention
  • Distributed training
  • Model benchmarking
  • Task-specific metrics
  • Generation quality
  • Human evaluation
  • LLM-as-a-judge concepts
  • Hallucination evaluation
  • Instruction-following evaluation
  • Base vs fine-tuned model comparison
  • Fine-tuning safety considerations
  • Bias and toxicity
  • Harmful-content evaluation
  • Privacy and sensitive data
  • Data licensing and provenance
  • Jailbreak resistance
  • Red teaming
  • Safety evaluation
  • Fine-tuning vs RAG
  • Domain-specific assistants
  • Retrieval-aware training
  • RAG evaluation
  • Enterprise knowledge customization
  • Structured output fine-tuning
  • Tool-use and function-calling datasets
  • Model quantization
  • 4-bit and 8-bit inference
  • GPTQ and AWQ concepts
  • Model compression
  • Model merging
  • Inference optimization
  • Latency and memory benchmarking
  • Local LLM deployment
  • vLLM
  • Optimized inference
  • API development
  • Docker deployment
  • GPU inference
  • Batch and streaming inference
  • Production serving
  • Experiment tracking
  • Model versioning
  • Dataset versioning
  • Training pipelines
  • Model registry
  • Continuous evaluation
  • Monitoring
  • Model and data drift
  • Production updates
Need customized curriculum?
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Hands On LLM Fine-Tuning Projects

Enroll in our LLM Fine-Tuning Course, gain practical experience fine-tuning large language models using instruction datasets, SFT, LoRA, QLoRA, preference optimization, evaluation, model optimization, and deployment for real-world AI applications.

Domain-Specific LLM Fine-Tuning Project

Fine-tune an open-source LLM using a domain-specific instruction dataset. Build a specialized AI assistant that generates relevant responses for areas such as healthcare, finance, education, or customer support.

Customer Support LLM Fine-Tuning & Deployment

Create a customer-support AI assistant by preparing conversational datasets and applying SFT with LoRA or QLoRA. Evaluate response quality and deploy the customized LLM through an API.

LLM Preference Optimization & Safety Project

Fine-tune an LLM using preference data and techniques such as DPO. Evaluate instruction-following, helpfulness, and safety, then optimize and deploy the model for a controlled real-world application.

For Corporates

Educate your workforce with new skills to improve their performance and productivity.

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Our Instructor
Name
Ms. Divya
Experience
10+ Years
Specialized in
LLM Fine-Tuning, Large Language Models, Generative AI, NLP, Transformer Architecture, Instruction Tuning, SFT, LoRA, QLoRA, PEFT, DPO, Model Optimization, and LLM Deployment.
More Details
Ms. Divya is an LLM professional with 10+ years of experience, specializing in Generative AI, NLP, Transformers, instruction tuning, SFT, LoRA, QLoRA, PEFT, DPO, optimization, and deployment.

LLM Fine-Tuning Course Objectives

Our LLM Fine-Tuning Course is designed to help learners master the complete process of customizing Large Language Models for domain-specific applications. Learners will gain practical knowledge of dataset preparation, instruction tuning, supervised fine-tuning, LoRA, QLoRA, PEFT and MLOps. Through hands-on projects, learners will develop the skills required to build, optimize, evaluate, and deploy production-ready fine-tuned LLM applications.

Basic Python programming knowledge is recommended. Familiarity with machine learning and deep learning concepts will also help learners understand the practical training workflows more effectively.

Yes. The course covers instruction tuning, supervised fine-tuning, dataset formatting, training workflows, hyperparameter selection, evaluation, and practical customization of LLMs.

Yes. Learners will understand preference optimization approaches such as Direct Preference Optimization (DPO) and how they can be used to align models with desired responses.

Learners will work on projects such as a domain-specific LLM fine-tuning system, customer support LLM fine-tuning and deployment, and an LLM preference optimization and safety project.

Job Assistance Program

Our Job Assistance Programme offers you special guidance through the course curriculum and helps in your interview preparation.

Specialised Curriculum
Get on-field knowledge and skills from our expert instructors.
Assessment
Upgrade your on-field skills with our assessments and track your progress in real time.
Hands-on Project
Our hands-on project help you gain experience in real-time working.
Certification Guidance
A global certificate always helps you stand out from the crowd.
Portfolio Building
Experts guide you to maximise your profile with current industry trends that employers expect.
Placment Cell
We promote your abilities and showcase your portfolio to employers.

LLM Fine-Tuning Career Opportunity

LLM Fine-Tuning offers careers as LLM Engineer, Generative AI Engineer, NLP Engineer, AI Engineer, and Machine Learning Engineer, with salaries varying by experience, skills, location, specialization, and employer.

Annual Pay Scale
Placement Designation
Employers
Annual Salary
Hiring Companies

Placement Guidance & Interview Preparation

Infibee’s placement guidance navigates you to your desired role in top organisations, ensuring you stand out and excel in every opportunity.

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I enrolled in the SAP ANALYTICS CLOUD course at Infibee Technologies and the classes were very well structured. The trainers possess industry experience and they delivered effective assistance with both resume creation and job placement services. This institution serves as the topmost educational center for SAP training courses.
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I completed the Workday HR course at Infibee Technologies and the training was really helpful for my career growth. The trainers support students by providing simple explanations which include real-world examples of every topic. Highly recommended to join now.
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LLM Fine-Tuning Course FAQ's

Infibee LLM Fine-Tuning Course offers wide range of services that suits for both fresher and experienced persons via both offline and online at your suitable time slots.

You need not worry about having missed a class. Our dedicated course coordinator will help them with anything and everything related to administration. The coordinator will arrange a session for the student with trainers in place of the missed one.

Yes, of course. You can contact our team at Infibee Technologies, and we will schedule a free demo or a conference call with our mentor for you.

We provide classroom, online, and self-based study material and recorded sessions for students based on their individual preferences.

Yes, all our trainers are industry professionals with extensive experience in their respective domains. They bring hands-on practical and real-world knowledge to the training sessions.

Yes, participants typically receive access to course materials, including recorded sessions, assignments, and additional resources, even after the training concludes.

We provide placement assistance to students, including resume building, interview preparation, and job placement support for a wide range of software courses.

Yes, we offer customisation of the syllabus for both individual candidates and corporate also.

Yes, we offer corporate training solutions. Companies can contact us for customised programmes tailored to their team’s needs.

Participants need a stable internet connection and a device (computer, laptop, or tablet) with the necessary software installed. Detailed technical requirements are provided upon enrollment.

In most cases, such requests can be accommodated. Participants can reach out to our support team to discuss their preferences and explore available options.

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