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Training Qwen - Deploying High-Performance Open-Source LLMs

Ref: PWB747
10 people max.
7000€ 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 advanced Qwen architectures for certified AI applications
  • Develop fine-tuning skills for Qwen tailored to enterprise needs
  • Implement RAG pipelines with Qwen for precise and scalable responses
  • Design autonomous AI agents based on Qwen in professional contexts
  • Optimize and deploy Qwen in production with advanced monitoring
  • Integrate Qwen into hybrid systems to boost team productivity

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.

Fouzi Benzidane
Fouzi Benzidane

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1Qwen Architectures: Analyze and Configure Advanced Models (Qwen-72B, Hugging Face Tools)

Dive into the specifics of Qwen models such as Qwen-72B and Qwen2, installation of optimized environments with Hugging Face Transformers and vLLM, dissection of attention layers and multilingual tokenizers, practical exercises on benchmark performance analysis, creation of a first fast local inference pipeline, with immediate trainer feedback to validate your expert skills.

Module 2Fine-tuning Qwen: Customize for Business Cases (LoRA, QLoRA, Custom Datasets)

Master LoRA and QLoRA techniques to fine-tune Qwen on proprietary datasets, data preparation with Tokenizers and PEFT, use of accelerators like DeepSpeed to scale on multiple GPUs, practical workshops on a realistic business case such as automated report generation, evaluation of perplexity and BLEU metrics, production of a deployable fine-tuned model ready for enterprise use.

Module 3RAG with Qwen: Integrate Retrieval for Contextualized Responses (FAISS, LangChain)

Build powerful RAG systems by coupling Qwen with vector databases FAISS and Pinecone, implementation of pipelines with LangChain and LlamaIndex, intelligent chunking of enterprise documents, exercises on complex multilingual queries, optimization of retrieval to reduce hallucinations, development of a RAG prototype application connected to your internal knowledge base, with live precision testing.

Module 4Qwen Agents: Orchestrate Autonomous AI Workflows (ReAct, Agentic Tools)

Develop intelligent agents based on Qwen using the ReAct pattern for reasoning and action, integration with external APIs via LangGraph, management of custom tools like search engines or SQL databases, practical simulations of enterprise scenarios such as automated customer support, debugging and chaining multi-step agents, creation of a capstone agent capable of complex tasks, directly boosting your professional productivity.

Module 5Qwen Deployment: Scale Securely in Production (Docker, Kubernetes, Prompt Security)

Deploy Qwen in high-performance inference mode with Docker, FastAPI, and Kubernetes for autoscaling, implementation of safeguards against jailbreaks and prompt injections, monitoring with Prometheus and Grafana for traceability, exercises on ONNX optimization and quantization for reduced GPU costs, production deployment of a complete Qwen service with CI/CD GitHub Actions, final security audit and enterprise maintenance plan.

Evaluation method

  • Multiple-choice quiz to validate learning outcomes at the end of the training
  • Continuous assessment through practical exercises
  • Presentation of the capstone project to the trainer

Learning method

  • Sessions led by an expert practitioner
  • Hands-on exercises based on real enterprise cases
  • Progressive capstone project throughout the training
  • Comprehensive course materials provided to each participant

Methods, materials and delivery

The Training Qwen - Deploying High-Performance Open-Source LLMs 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 Qwen - Deploying High-Performance Open-Source 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 Qwen - Deploying High-Performance Open-Source 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

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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