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Training QLoRA - Efficiently Fine-Tune Giant LLMs

Course created on July 27, 2026Updated on October 5, 2026

On quote$3,465

3 days
Onsite
10 people max.

Request a quote See the program

Ref: CRS105

10 people max.

On quote$3,465

In-company rate: on quotefrom $6,290 for the whole team

On-site and certification exam on quote

3 days

Onsite

Don't let this gap widen

Why this program matters

Without QLoRA mastery, ML teams waste up to 80% of GPU resources on traditional fine-tuning, with cloud costs exceeding 10 000€ per project.
Unoptimized models generate 45% more errors in production, leading to 2M€ annual losses for AI companies.
In 2026, 68% of senior ML positions will require QLoRA, excluding uncertified profiles.
Every quarter without QLoRA skills widens the competitive gap, exposing your company to critical delays on generative AI.

Trusted by — schools, companies and industrial groups

25,719 programs · 25 countries

Every program built with a dedicated engineer

Companies: Aptar, ArcelorMittal, Equans, EDF, Ubisoft and Microsoft.

Learning objectives

Master QLoRA to optimize LLM fine-tuning in enterprise settings
Develop certified skills in 4-bit quantization of massive models
Implement scalable QLoRA pipelines with PEFT and BitsAndBytes
Design low-rank adaptation strategies for professional performance
Optimize GPU resources during LLM training in production
Deploy QLoRA fine-tuned models in secure enterprise environments

Program

Module 1QLoRA Fundamentals: Configuration and Quantization of LLMs (PEFT, BitsAndBytes, 4-bit)

Dive into advanced QLoRA installation via Hugging Face and PyTorch, quantize your first LLMs in 4-bit with BitsAndBytes to drastically reduce GPU memory, implement low-rank LoRA adapters on models like Llama or Mistral, perform practical fine-tuning exercises on enterprise datasets, measure gains in speed and accuracy, and produce your first production-ready optimized checkpoints.

Module 2Advanced QLoRA: Fine-Tuning Pipelines and Optimization (Double Quantization, Gradient Checkpointing)

Build complete QLoRA pipelines with double NF4 and FP16 quantization, integrate gradient checkpointing and paged optimizers to scale on multi-GPU, fine-tune LLMs on concrete cases like code generation or professional sentiment analysis, test advanced hyperparameters via automated sweeps, analyze perplexity and loss metrics, and generate performance reports demonstrating 70% resource savings for your enterprise projects.

Module 3QLoRA Deployment: Optimized Inference and Production Integration (vLLM, TensorRT-LLM)

Deploy your fine-tuned QLoRA models with vLLM for ultra-fast inference, optimize for TensorRT-LLM on NVIDIA hardware, integrate into secure APIs for enterprise applications, perform before/after QLoRA comparative benchmarks, manage LoRA adapter fusion into full models, and produce a final deliverable: a scalable demo app with an industrialization plan and performance monitoring under real conditions.

Evaluation method

Expert multiple-choice quiz to validate learning outcomes at the end of the training
Continuous evaluation through practical QLoRA exercises
Defense of the red thread project for LLM fine-tuning

Learning method

Courses led by an active AI expert trainer
Practical exercises on real enterprise cases in QLoRA
Progressive red thread project for fine-tuning massive LLMs
Complete course materials provided to each participant

Methods, materials and delivery

The Training QLoRA - Efficiently Fine-Tune Giant LLMs program is delivered onsite or remote (blended-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.

The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.

Equipment required

For the smooth delivery of the Training QLoRA - Efficiently Fine-Tune Giant LLMs program, the following equipment is required:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, hands-on exercises and complementary resources
Post-training access to materials and educational resources

For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.

* contact us for remote delivery feasibility** ratio varies depending on the program

Skills assessment methods

Assessment of skills acquired during the Training QLoRA - Efficiently Fine-Tune Giant LLMs program is performed through:

During training: case studies, hands-on labs and professional scenarios
End of training: self-assessment questionnaire and skills evaluation by the trainer
After training: completion certificate detailing acquired skills

Program accessibility

Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.

Enrollment terms and lead times

Registration is possible up to 48 business hours before the start of training. All our programs are built for corporate L&D budgets and delivered onsite or remotely.

The Learni story

Founded by engineers and learning experts, Learni's mission is to make high-impact tech training accessible to teams everywhere. We work remotely with organizations across the US and Canada, in your time zone, to help teams upskill fast.

Our method

Training quality, guaranteed at every step

Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.

Step 1

Rigorous trainer selection

Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.

Triple validation: technical, pedagogical, sectoral.
Minimum rating 4.8/5 over the last 12 sessions.

Step 2

You meet the trainer beforehand

30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.

Live briefing on goals and team context.
Veto right — we swap the trainer for free if needed.

Step 3

Content tailored to your context

No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.

Hands-on cases drawn from your stack and projects.
Program co-written then validated by your team.

Step 4

Continuous quality follow-up

Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.

NPS, knowledge quizzes and skills self-assessment.
Satisfaction guarantee: fully satisfied or free rework.

A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.

A child walking to school with a backpack

Our social commitment

A school kit donated to a child for every training booked

To fight inequalities in access to education, Learni donates a complete school kit to a child in need for every training booked. You build your skills, a child heads back to school.

Backpack, notebooks and essential supplies
Distributed through our partner charities
Included, at no extra cost to you

Enroll and offer a kit

FAQ

Frequently asked questions

Which tools are used in QLoRA training?+

Hands-on work runs on PEFT, bitsAndBytes, 4-bit, double Quantization, gradient Checkpointing and vLLM. Those tools come from the QLoRA program itself, delivered on site over 3 days, so participants practise on the stack they will use at work.

What can participants do after QLoRA training?+

They can master QLoRA to optimize LLM fine-tuning in enterprise settings, develop certified skills in 4-bit quantization of massive models and implement scalable QLoRA pipelines with PEFT and BitsAndBytes. Those are the stated objectives of the QLoRA program at Expert level, checked by the end-of-session assessment.

What does the QLoRA program cover, day by day?+

Day 1: QLoRA Fundamentals: Configuration and Quantization of LLMs (PEFT, BitsAndBytes, 4-bit). Day 2: Advanced QLoRA: Fine-Tuning Pipelines and Optimization (Double Quantization, Gradient Checkpointing). Day 3: QLoRA Deployment: Optimized Inference and Production Integration (vLLM, TensorRT-LLM). The QLoRA program runs over 3 days, delivered on site.

How is QLoRA training assessed?+

Assessment relies on expert multiple-choice quiz to validate learning outcomes at the end of the training, continuous evaluation through practical QLoRA exercises and defense of the red thread project for LLM fine-tuning. Learni issues a completion certificate together with the individual assessment report at the end of the QLoRA program, which an L&D team can file as evidence.

How much does QLoRA training cost per participant?+

QLoRA training is priced on quote. The amount depends on headcount, format and how far the syllabus is tailored to your team. A detailed quote is sent within one business day, and team pricing covers the whole group from the second participant.

How long does QLoRA training take?+

3 days, available live online (US time zones) or on-site at your offices. Half-day tracks spread over several weeks are possible so the team stays operational.

Who pays for QLoRA training?+

Most US teams pay directly through their company L&D or training budget. Learni invoices in US dollars and accepts bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.

What do participants need to know before QLoRA training?+

Advanced expertise in PyTorch, Hugging Face Transformers, LLM fine-tuning, and GPU cluster usage.

Is a certificate issued after QLoRA training?+

Yes. A Learni completion certificate is issued, along with the individual evaluation report. Learni is a certified training provider and prepares for third-party certifications, but does not award them itself.

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