Fine-Tuning Large Language Models Using QLoRA Training Course
QLoRA represents an sophisticated methodology for refining large language models (LLMs) by utilizing quantization techniques, thereby providing a more cost-effective approach to model refinement without necessitating substantial computational resources. This educational program addresses both the theoretical underpinnings and practical application of refining LLMs through QLoRA.
This instructor-led, live training session (available online or at a physical location) targets machine learning engineers, AI developers, and data scientists with intermediate to advanced proficiency levels who aim to master the efficient refinement of large models for specific tasks and custom modifications using QLoRA.
Upon completion of this training, participants will be capable of:
- Comprehending the theoretical basis of QLoRA and quantization strategies for LLMs.
- Applying QLoRA to refine large language models for domain-specific use cases.
- Enhancing refinement performance on constrained computational hardware via quantization.
- Efficiently deploying and evaluating refined models within real-world contexts.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Live-lab implementation exercises.
Customization Options
- For inquiries regarding customized training for this course, please reach out to us to arrange details.
Course Outline
Introduction to QLoRA and Quantization
- Overview of quantization and its role in model optimization
- Introduction to QLoRA framework and its benefits
- Key differences between QLoRA and traditional fine-tuning methods
Fundamentals of Large Language Models (LLMs)
- Introduction to LLMs and their architecture
- Challenges of fine-tuning large models at scale
- How quantization helps overcome computational constraints in LLM fine-tuning
Implementing QLoRA for Fine-Tuning LLMs
- Setting up the QLoRA framework and environment
- Preparing datasets for QLoRA fine-tuning
- Step-by-step guide to implementing QLoRA on LLMs using Python and PyTorch/TensorFlow
Optimizing Fine-Tuning Performance with QLoRA
- How to balance model accuracy and performance with quantization
- Techniques for reducing compute costs and memory usage during fine-tuning
- Strategies for fine-tuning with minimal hardware requirements
Evaluating Fine-Tuned Models
- How to assess the effectiveness of fine-tuned models
- Common evaluation metrics for language models
- Optimizing model performance post-tuning and troubleshooting issues
Deploying and Scaling Fine-Tuned Models
- Best practices for deploying quantized LLMs into production environments
- Scaling deployment to handle real-time requests
- Tools and frameworks for model deployment and monitoring
Real-World Use Cases and Case Studies
- Case study: Fine-tuning LLMs for customer support and NLP tasks
- Examples of fine-tuning LLMs in various industries like healthcare, finance, and e-commerce
- Lessons learned from real-world deployments of QLoRA-based models
Summary and Next Steps
Requirements
- Knowledge of machine learning fundamentals and neural network architectures
- Experience with model fine-tuning and transfer learning
- Proficiency with large language models (LLMs) and deep learning frameworks (e.g., PyTorch, TensorFlow)
Audience
- Machine learning engineers
- AI developers
- Data scientists
Open Training Courses require 5+ participants.
Fine-Tuning Large Language Models Using QLoRA Training Course - Booking
Fine-Tuning Large Language Models Using QLoRA Training Course - Enquiry
Fine-Tuning Large Language Models Using QLoRA - Consultancy Enquiry
Upcoming Courses
Related Courses
Advanced LangGraph: Optimization, Debugging, and Monitoring Complex Graphs
35 HoursLangGraph is a framework designed for constructing stateful, multi-actor LLM applications as composable graphs, featuring persistent state and precise control over execution.
This instructor-led, live training (available online or onsite) targets advanced-level AI platform engineers, AI DevOps specialists, and ML architects who aim to optimize, debug, monitor, and manage production-grade LangGraph systems.
By the end of this training, participants will be able to:
- Design and optimize complex LangGraph topologies for speed, cost efficiency, and scalability.
- Enhance reliability through retries, timeouts, idempotency, and checkpoint-based recovery.
- Debug and trace graph executions, inspect state, and systematically reproduce production issues.
- Instrument graphs with logs, metrics, and traces, deploy to production, and monitor SLAs and costs.
Format of the Course
- Interactive lecture and discussion.
- Extensive exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Building Coding Agents with Devstral: From Agent Design to Tooling
14 HoursDevstral is an open-source framework specifically crafted for creating and operating coding agents. These agents can interact with codebases, developer tools, and APIs to significantly boost engineering productivity.
This instructor-led live training (available online or onsite) targets intermediate to advanced ML engineers, developer-tooling teams, and SREs who aim to design, implement, and optimize coding agents using Devstral.
Upon completing this training, participants will be able to:
- Set up and configure Devstral for coding agent development.
- Design agentic workflows for exploring and modifying codebases.
- Integrate coding agents with developer tools and APIs.
- Implement best practices for secure and efficient agent deployment.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live-lab environment.
Customization Options
- To request customized training for this course, please contact us to arrange.
Open-Source Model Ops: Self-Hosting, Fine-Tuning and Governance with Devstral & Mistral Models
14 HoursDevstral and Mistral models are open-source AI technologies designed for flexible deployment, fine-tuning, and scalable integration.
This instructor-led, live training (available online or onsite) targets intermediate to advanced machine learning engineers, platform teams, and research engineers who wish to self-host, fine-tune, and govern Mistral and Devstral models in production environments.
By the end of this training, participants will be able to:
- Set up and configure self-hosted environments for Mistral and Devstral models.
- Apply fine-tuning techniques for domain-specific performance.
- Implement versioning, monitoring, and lifecycle governance.
- Ensure security, compliance, and responsible usage of open-source models.
Format of the Course
- Interactive lecture and discussion.
- Hands-on exercises in self-hosting and fine-tuning.
- Live-lab implementation of governance and monitoring pipelines.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
LangGraph Applications in Finance
35 HoursLangGraph serves as a framework designed for constructing stateful, multi-agent LLM applications through composable graphs that maintain persistent state and precise execution control.
This instructor-led live training, available either online or on-site, targets intermediate to advanced professionals looking to design, implement, and operate LangGraph-based financial solutions that ensure proper governance, observability, and compliance.
Upon completion of this training, participants will be capable of:
- Designing finance-specific LangGraph workflows that align with regulatory and audit requirements.
- Integrating financial data standards and ontologies into graph states and tooling.
- Implementing reliability, safety, and human-in-the-loop controls for critical processes.
- Deploying, monitoring, and optimizing LangGraph systems to achieve desired performance, cost efficiency, and SLAs.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live laboratory environment.
Customization Options
- To request tailored training for this course, please contact us to arrange it.
LangGraph Foundations: Graph-Based LLM Prompting and Chaining
14 HoursLangGraph is a framework for building graph-structured LLM applications that support planning, branching, tool use, memory, and controllable execution.
This instructor-led, live training (online or onsite) is aimed at beginner-level developers, prompt engineers, and data practitioners who wish to design and build reliable, multi-step LLM workflows using LangGraph.
By the end of this training, participants will be able to:
- Explain core LangGraph concepts (nodes, edges, state) and when to use them.
- Build prompt chains that branch, call tools, and maintain memory.
- Integrate retrieval and external APIs into graph workflows.
- Test, debug, and evaluate LangGraph apps for reliability and safety.
Format of the Course
- Interactive lecture and facilitated discussion.
- Guided labs and code walkthroughs in a sandbox environment.
- Scenario-based exercises on design, testing, and evaluation.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
LangGraph in Healthcare: Workflow Orchestration for Regulated Environments
35 HoursLangGraph facilitates stateful, multi-agent workflows driven by Large Language Models (LLMs), offering precise control over execution paths and state persistence. In the healthcare sector, these features are essential for ensuring compliance, interoperability, and the development of decision-support systems that seamlessly align with medical workflows.
This instructor-led training, available both online and on-site, targets intermediate to advanced professionals seeking to design, implement, and manage healthcare solutions using LangGraph, while addressing regulatory, ethical, and operational challenges.
Upon completion of this course, participants will be equipped to:
- Design healthcare-specific LangGraph workflows that prioritize compliance and auditability.
- Integrate LangGraph applications with medical ontologies and standards, including FHIR, SNOMED CT, and ICD.
- Apply best practices for reliability, traceability, and explainability in sensitive environments.
- Deploy, monitor, and validate LangGraph applications within healthcare production settings.
Course Format
- Interactive lectures and discussions.
- Hands-on exercises featuring real-world case studies.
- Implementation practice in a live-lab environment.
Customization Options
- To request customized training for this course, please contact us to arrange.
LangGraph for Legal Applications
35 HoursLangGraph serves as a framework for constructing stateful, multi-agent LLM applications through composable graphs that maintain persistent state and offer precise execution control.
This instructor-led live training (available online or onsite) is designed for intermediate to advanced professionals aiming to design, implement, and operate LangGraph-based legal solutions with essential compliance, traceability, and governance controls.
By the end of this training, participants will be able to:
- Design legal-specific LangGraph workflows that ensure auditability and compliance.
- Integrate legal ontologies and document standards into graph state and processing.
- Implement guardrails, human-in-the-loop approvals, and traceable decision paths.
- Deploy, monitor, and maintain LangGraph services in production with observability and cost controls.
Format of the Course
- Interactive lecture and discussion.
- Extensive exercises and practical application.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Building Dynamic Workflows with LangGraph and LLM Agents
14 HoursLangGraph is a framework for composing graph-structured LLM workflows that support branching, tool use, memory, and controllable execution.
This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers and product teams who wish to combine LangGraph’s graph logic with LLM agent loops to build dynamic, context-aware applications such as customer support agents, decision trees, and information retrieval systems.
By the end of this training, participants will be able to:
- Design graph-based workflows that coordinate LLM agents, tools, and memory.
- Implement conditional routing, retries, and fallbacks for robust execution.
- Integrate retrieval, APIs, and structured outputs into agent loops.
- Evaluate, monitor, and harden agent behavior for reliability and safety.
Format of the Course
- Interactive lecture and facilitated discussion.
- Guided labs and code walkthroughs in a sandbox environment.
- Scenario-based design exercises and peer reviews.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
LangGraph for Marketing Automation
14 HoursLangGraph is a graph-based orchestration framework that enables conditional, multi-step LLM and tool workflows, ideal for automating and personalizing content pipelines.
This instructor-led, live training (online or onsite) is aimed at intermediate-level marketers, content strategists, and automation developers who wish to implement dynamic, branching email campaigns and content generation pipelines using LangGraph.
By the end of this training, participants will be able to:
- Design graph-structured content and email workflows with conditional logic.
- Integrate LLMs, APIs, and data sources for automated personalization.
- Manage state, memory, and context across multi-step campaigns.
- Evaluate, monitor, and optimize workflow performance and delivery outcomes.
Format of the Course
- Interactive lectures and group discussions.
- Hands-on labs implementing email workflows and content pipelines.
- Scenario-based exercises on personalization, segmentation, and branching logic.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Le Chat Enterprise: Private ChatOps, Integrations & Admin Controls
14 HoursLe Chat Enterprise delivers a secure, customizable, and governed ChatOps solution, empowering organizations with conversational AI capabilities while supporting RBAC, SSO, connectors, and enterprise application integrations.
This instructor-led live training, available both online and onsite, is designed for intermediate-level product managers, IT leads, solution engineers, and security or compliance teams looking to deploy, configure, and govern Le Chat Enterprise within their enterprise environments.
Upon completion of this course, participants will be equipped to:
- Deploy and configure Le Chat Enterprise for secure operations.
- Implement RBAC, SSO, and compliance-oriented controls.
- Connect Le Chat Enterprise with enterprise applications and data repositories.
- Create and execute governance and administrative playbooks for ChatOps.
Course Format
- Engaging lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live laboratory environment.
Customization Options
- For tailored training requests, please contact us to arrange.
Cost-Effective LLM Architectures: Mistral at Scale (Performance / Cost Engineering)
14 HoursMistral is a high-performance family of large language models optimized for cost-effective production deployment at scale.
This instructor-led, live training (online or onsite) is aimed at advanced-level infrastructure engineers, cloud architects, and MLOps leads who wish to design, deploy, and optimize Mistral-based architectures for maximum throughput and minimum cost.
By the end of this training, participants will be able to:
- Implement scalable deployment patterns for Mistral Medium 3.
- Apply batching, quantization, and efficient serving strategies.
- Optimize inference costs while maintaining performance.
- Design production-ready serving topologies for enterprise workloads.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Productizing Conversational Assistants with Mistral Connectors & Integrations
14 HoursMistral AI provides an open-source AI platform that empowers teams to create and embed conversational assistants within enterprise environments and customer-facing processes.
This instructor-led live training, available online or on-site, targets beginner to intermediate product managers, full-stack developers, and integration engineers looking to design, integrate, and commercialize conversational assistants via Mistral connectors and integrations.
Upon completing this training, participants will be able to:
- Connect Mistral conversational models with enterprise and SaaS connectors.
- Implement retrieval-augmented generation (RAG) to ensure grounded responses.
- Create UX patterns for both internal and external chat assistants.
- Deploy assistants into product workflows for practical use cases.
Course Format
- Interactive lectures and discussions.
- Practical integration exercises.
- Live-lab development of conversational assistants.
Customization Options
- To request a customized version of this course, please contact us to arrange it.
Enterprise-Grade Deployments with Mistral Medium 3
14 HoursMistral Medium 3 is a high-performance, multimodal large language model designed for production-grade deployment across enterprise environments.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level AI/ML engineers, platform architects, and MLOps teams who wish to deploy, optimize, and secure Mistral Medium 3 for enterprise use cases.
By the end of this training, participants will be able to:
- Deploy Mistral Medium 3 using API and self-hosted options.
- Optimize inference performance and costs.
- Implement multimodal use cases with Mistral Medium 3.
- Apply security and compliance best practices for enterprise environments.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Mistral for Responsible AI: Privacy, Data Residency & Enterprise Controls
14 HoursMistral AI serves as an open, enterprise-grade AI platform offering capabilities for the secure, compliant, and responsible deployment of artificial intelligence.
This instructor-led training session, available both online and on-site, is designed for compliance leads, security architects, and legal and operations stakeholders at an intermediate level. The course focuses on implementing responsible AI practices using Mistral by leveraging privacy protections, data residency controls, and enterprise management mechanisms.
Upon completion of this training, participants will be equipped to:
- Deploy privacy-preserving techniques within Mistral environments.
- Execute data residency strategies to satisfy regulatory obligations.
- Establish enterprise-grade controls, including RBAC, SSO, and audit logging.
- Assess vendor and deployment alternatives to ensure compliance alignment.
Course Format
- Interactive lectures and discussions.
- Case studies and exercises centered on compliance.
- Practical implementation of enterprise AI controls.
Customization Options
- For tailored training on this subject, please contact us to make arrangements.
Multimodal Applications with Mistral Models (Vision, OCR, & Document Understanding)
14 HoursMistral models are open-source AI technologies that have evolved to support multimodal workflows, handling both linguistic and visual tasks for enterprise and research purposes.
This instructor-led live training, available online or onsite, targets intermediate-level ML researchers, applied engineers, and product teams aiming to construct multimodal applications with Mistral models, including pipelines for OCR and document understanding.
Upon completing this training, participants will be equipped to:
- Configure and set up Mistral models for multimodal tasks.
- Implement OCR workflows and integrate them with NLP pipelines.
- Develop document understanding applications tailored for enterprise use cases.
- Create vision-text search and assistive UI functionalities.
Course Format
- Interactive lectures and discussions.
- Practical coding exercises.
- Live-lab implementation of multimodal pipelines.
Customization Options
- To request a customized training session for this course, please contact us to arrange.
