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Training LoRA Fine-Tuning - Adapting LLMs for the Enterprise

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

On quote$5,775

5 days
Onsite
10 people max.

Request a quote See the program

Ref: DIB193

10 people max.

On quote$5,775

In-company rate: on quotefrom $10,395 for the whole team

On-site and certification exam on quote

5 days

Onsite

Don't let this gap widen

Why this program matters

Without mastery of LoRA fine-tuning, companies waste up to 90% of GPU resources on unnecessary full fine-tunings, with cloud costs exceeding 10 000€ per project.
In 2026, 68% of AI deployments fail due to lack of customized adaptation, resulting in 25% productivity loss for data teams.
Untrained professionals risk obsolescence against competitors delivering custom LLMs in days instead of months.
Every quarter without these skills widens an insurmountable competitive gap in 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 the principles of LoRA to efficiently fine-tune large language models
Develop skills in GPU resource optimization during professional fine-tuning
Implement LoRA on open-source LLMs for certifying enterprise use cases
Design scalable and reproducible LoRA fine-tuning pipelines
Optimize the performance of adapted models with quantitative evaluation
Deploy LoRA fine-tuned models in secure production environments
Integrate LoRA into enterprise workflows for personalized AI

Program

Module 1LoRA Fundamentals: Principles and Environment Setup (PyTorch, Hugging Face)

Dive into the mechanisms of Low-Rank Adaptation to drastically reduce fine-tuning costs, install the environment with PEFT and Hugging Face Transformers, configure a first base model like Llama or Mistral, perform initial tests on enterprise datasets, produce your first functional LoRA adapter with practical exercises and personalized code review to validate professional skills.

Module 2LoRA Implementation: Fine-tuning on Custom Datasets (Tokenizers, Data Loaders)

Prepare and tokenize your specific business datasets, integrate optimized data loaders for LoRA, launch full fine-tuning on an LLM with advanced hyperparameters like rank and alpha, monitor metrics in real-time via Weights & Biases, apply regularization techniques to avoid overfitting, generate performance reports, and iterate on concrete enterprise cases for immediate and certifying results.

Module 3LoRA Optimization: Quantization and Multi-GPU (bitsandbytes, DeepSpeed)

Advance to optimization with 4-bit and 8-bit quantization via bitsandbytes, set up multi-GPU sessions to accelerate LoRA fine-tuning on large data volumes, integrate DeepSpeed for ZeRO-Offload, test on high-scale enterprise scenarios, measure time and memory gains, produce ultra-lightweight models ready for deployment with comparative benchmarks to demonstrate professional value.

Module 4LoRA Evaluation and Merging: Advanced Metrics and Model Fusion

Develop comprehensive evaluation scripts with ROUGE, BLEU, and perplexity on your fine-tuned LoRA models, conduct human and automated A/B tests, merge LoRA adapters into the base model using mergekit, optimize for fast inference, analyze biases and robustness on real enterprise cases, generate deliverables like interactive dashboards for certifying validation and smooth production integration.

Module 5LoRA Deployment: CI/CD Pipelines and Serving (vLLM, Docker, Kubernetes)

Containerize your fine-tuned LoRA models with Docker for maximum portability, deploy via Kubernetes or vLLM servers for high-performance inference, integrate CI/CD pipelines with GitHub Actions, secure API endpoints for enterprise use, simulate production loads with stress tests, finalize a complete red thread project with documentation and live deployment, ready for immediate and certifying implementation.

Evaluation method

Technical MCQ on LoRA and fine-tuning at the end of the training
Practical evaluation by fine-tuning a personal model
Defense of the red thread LoRA project in front of experts

Learning method

Courses delivered by expert AI trainers in active professional roles
Hands-on exercises on real enterprise datasets and LLMs
Progressive red thread LoRA project over 5 days
Detailed course materials and provided open-source resources

Methods, materials and delivery

The Training LoRA Fine-Tuning - Adapting LLMs for the Enterprise 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 LoRA Fine-Tuning - Adapting LLMs for the Enterprise 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 LoRA Fine-Tuning - Adapting LLMs for the Enterprise 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 LoRA Fine-Tuning training?+

Hands-on work runs on pyTorch, hugging Face, tokenizers, data Loaders, bitsandbytes and deepSpeed. Those tools come from the LoRA Fine-Tuning program itself, delivered on site over 5 days, so participants practise on the stack they will use at work.

What can participants do after LoRA Fine-Tuning training?+

They can master the principles of LoRA to efficiently fine-tune large language models, develop skills in GPU resource optimization during professional fine-tuning and implement LoRA on open-source LLMs for certifying enterprise use cases. Those are the stated objectives of the LoRA Fine-Tuning program at Advanced level, checked by the end-of-session assessment.

What does the LoRA Fine-Tuning program cover, day by day?+

Day 1: LoRA Fundamentals: Principles and Environment Setup (PyTorch, Hugging Face). Day 2: LoRA Implementation: Fine-tuning on Custom Datasets (Tokenizers, Data Loaders). Day 3: LoRA Optimization: Quantization and Multi-GPU (bitsandbytes, DeepSpeed). Day 4: LoRA Evaluation and Merging: Advanced Metrics and Model Fusion. Day 5: LoRA Deployment: CI/CD Pipelines and Serving (vLLM, Docker, Kubernetes). The LoRA Fine-Tuning program runs over 5 days, delivered on site.

How is LoRA Fine-Tuning training assessed?+

Assessment relies on technical MCQ on LoRA and fine-tuning at the end of the training, practical evaluation by fine-tuning a personal model and defense of the red thread LoRA project in front of experts. Learni issues a completion certificate together with the individual assessment report at the end of the LoRA Fine-Tuning program, which an L&D team can file as evidence.

How much does LoRA Fine-Tuning training cost per participant?+

LoRA Fine-Tuning 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 LoRA Fine-Tuning training take?+

5 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 LoRA Fine-Tuning 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 LoRA Fine-Tuning training?+

Mastery of Python, PyTorch or TensorFlow, basics of transformers and LLM fine-tuning.

Is a certificate issued after LoRA Fine-Tuning 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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