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Training Supervised Fine-Tuning (SFT) - Adapting Efficient AI Models

Ref: PXU580
10 people max.
4375€ HT / per person
−15% from 2 people−30% from 3 people−50% from 5 people
Pay in 3 installments · +$170/day onsite · +$500 with certification exam
5 journées
distanciel

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Learning objectives

  • Master the fundamentals of Supervised Fine-Tuning for professional applications
  • Prepare qualified datasets adapted to SFT in business settings
  • Implement supervised fine-tuning with Hugging Face and PyTorch
  • Evaluate and optimize SFT models for certified performance
  • Deploy fine-tuned models in production environments
  • Develop SFT skills to accelerate AI innovation

The Learni story

Founded by passionate learning and innovation experts, Learni's mission is to make professional training accessible to everyone, anywhere in the world. Our team operates in major hubs — London, New York, Boston — and internationally, to support talents and organizations in upskilling.

Don't let this gap widen

Why this program matters

  • Without this upskilling, your team accumulates a technological gap that translates directly into productivity loss.

  • Organizations that don't train their talents on key topics see their competitiveness drop.

  • Every quarter without training is a gap widening with competitors who invest.

  • The cost of inaction quickly exceeds that of well-targeted training.

Allan Busi
Allan Busi

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1SFT Fundamentals: Installation and First Concepts (Hugging Face, PyTorch, datasets)

Introduction to the principles of Supervised Fine-Tuning, complete installation of the environment with Hugging Face Transformers and PyTorch, creation of a first SFT project on a pre-trained BERT model, practical exercises to understand the difference between pre-training and fine-tuning, manipulation of public datasets like GLUE, generation of your first model adapted to a simple text classification task, with immediate trainer feedback to consolidate professional basics.

Module 2SFT Dataset Preparation: Cleaning and Annotation (LabelStudio, Pandas, tokenizers)

In-depth analysis of data requirements for SFT, hands-on with Pandas and Hugging Face tokenizers to clean and structure your business datasets, use of LabelStudio for efficient supervised example annotation, real-world cases on textual data such as customer feedback, creation of optimized train/validation/test splits, collaborative exercises to produce a dataset ready for fine-tuning, directly enhancing your AI data management skills.

Module 3SFT Implementation: Supervised Training (Trainer API, GPU acceleration)

Configuration of the Hugging Face Trainer API to launch supervised fine-tuning on GPU, parameterization of hyperparameters such as learning rate and batch size adapted to professional constraints, practical training on a model like DistilBERT for an NER task, real-time monitoring with TensorBoard, resolution of common pitfalls like overfitting via early stopping, production of saved checkpoints, transforming your ideas into performant and deployable models.

Module 4SFT Evaluation: Metrics and Optimization (BLEU, F1-score, LoRA)

Rigorous evaluation of SFT models with key metrics like accuracy, F1-score, and perplexity on test datasets, before/after fine-tuning comparison to quantify gains, introduction to LoRA for efficient fine-tuning on limited resources, iterative optimization via hyperparameter tuning with Optuna, business case on personalized text generation, exercises to refine your red thread model, ensuring certified performance and immediate business value.

Module 5SFT Deployment: Production and Advanced Cases (FastAPI, Docker, ONNX)

Deployment of SFT models as APIs with FastAPI and Docker for seamless business integration, conversion to ONNX for inference acceleration, end-to-end testing on real scenarios like supervised chatbots, production securing and monitoring with Prometheus, finalization of the red thread project with live deployment, code review and personalized improvement plan, equipping you to innovate rapidly in your professional AI projects.

Evaluation method

  • Multiple-choice quiz to validate acquired knowledge at the end of the training
  • Continuous evaluation through practical SFT exercises
  • Presentation of the red thread project on supervised fine-tuning

Learning method

  • Courses led by an expert trainer in applied AI
  • Practical exercises on real business cases in SFT
  • Progressive red thread project in Supervised Fine-Tuning
  • Complete course materials and Hugging Face resources provided

Methods, materials and delivery

The Training Supervised Fine-Tuning (SFT) - Adapting Efficient AI Models program is delivered onsite or remote (blended-learning, e-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 Supervised Fine-Tuning (SFT) - Adapting Efficient AI Models 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 Supervised Fine-Tuning (SFT) - Adapting Efficient AI Models 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

Learni programs are available inter-company and intra-company, onsite or remote. Enrollments are possible up to 48 business hours before the program starts. Our programs are eligible for corporate funding paths. Contact us to discuss your training project and funding options.

Verified reviews

What our learners

4.9 · +100 verified reviews
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
Read all reviews
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.

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