Fine-Tuning Large Language Models Using QLoRA Training Course
QLoRA represents an advanced methodology for fine-tuning large language models (LLMs) by utilizing quantization techniques, providing a highly efficient approach to model adaptation without the burden of excessive computational expenses. This session encompasses both the theoretical underpinnings and the practical application of applying QLoRA to fine-tune LLMs.
This instructor-led, live training, available online or onsite, is designed for machine learning engineers, AI developers, and data scientists at intermediate to advanced levels who aim to master the use of QLoRA to efficiently adapt large models for specific tasks and custom requirements.
Upon completion of this training, participants will be capable of:
- Grasping the theoretical principles behind QLoRA and quantization techniques for LLMs.
- Executing QLoRA implementations to fine-tune large language models for domain-specific applications.
- Enhancing fine-tuning performance within constrained computational resources through quantization.
- Efficiently deploying and evaluating fine-tuned models in practical, real-world scenarios.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live laboratory environment.
Customization Options
- To arrange a customized version of this training, please contact us directly.
Course Outline
Introduction to QLoRA and Quantization
- Overview of quantization and its critical role in model optimization
- Introduction to the QLoRA framework and its associated benefits
- Key distinctions between QLoRA and traditional fine-tuning methods
Fundamentals of Large Language Models (LLMs)
- Introduction to LLMs and their architectural design
- Challenges associated with fine-tuning large models at scale
- How quantization aids in overcoming computational constraints during LLM fine-tuning
Implementing QLoRA for Fine-Tuning LLMs
- Setting up the QLoRA framework and development environment
- Preparing datasets specifically for QLoRA fine-tuning
- Step-by-step guidance on implementing QLoRA on LLMs using Python and PyTorch/TensorFlow
Optimizing Fine-Tuning Performance with QLoRA
- Balancing model accuracy and performance through quantization
- Techniques for reducing compute costs and memory footprint during fine-tuning
- Strategies for fine-tuning with minimal hardware requirements
Evaluating Fine-Tuned Models
- Methods for assessing the effectiveness of fine-tuned models
- Common evaluation metrics used for language models
- Post-tuning performance optimization and troubleshooting strategies
Deploying and Scaling Fine-Tuned Models
- Best practices for deploying quantized LLMs into production environments
- Scaling deployments to manage real-time requests effectively
- Tools and frameworks essential 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 LLM fine-tuning across industries such as healthcare, finance, and e-commerce
- Key lessons learned from real-world deployments of QLoRA-based models
Summary and Next Steps
Requirements
- A solid understanding of machine learning fundamentals and neural networks
- Practical experience with model fine-tuning and transfer learning
- Familiarity 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 serves as a framework for constructing stateful, multi-actor LLM applications as composable graphs that maintain persistent state and provide granular control over execution.
This instructor-led live training, available online or onsite, is designed for advanced AI platform engineers, DevOps professionals specializing in AI, and ML architects who aim to optimize, debug, monitor, and operate production-grade LangGraph systems.
Upon completing this training, participants will be able to:
- Design and optimize complex LangGraph topologies to enhance speed, reduce costs, and ensure scalability.
- Engineer reliability by implementing retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
- Debug and trace graph executions, inspect state, and systematically reproduce production issues.
- Instrument graphs with logs, metrics, and traces, deploy them to production, and monitor SLAs and associated costs.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training version of this course, please contact us to make arrangements.
Building Coding Agents with Devstral: From Agent Design to Tooling
14 HoursDevstral is an open-source framework engineered for the creation and operation of coding agents capable of interacting with codebases, developer utilities, and APIs to boost engineering productivity.
This instructor-led, live training session, available either 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 completion of this training, participants will be equipped to:
- Configure and set up Devstral for coding agent development.
- Design agentic workflows tailored for codebase exploration and modification.
- Seamlessly integrate coding agents with developer tools and APIs.
- Apply best practices to ensure 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 a customized training curriculum for this course, please contact us for arrangement.
Open-Source Model Ops: Self-Hosting, Fine-Tuning and Governance with Devstral & Mistral Models
14 HoursMistral and Devstral are open-source AI technologies engineered for flexible deployment, fine-tuning, and scalable integration.
This instructor-led, live training—available online or onsite—is designed for intermediate to advanced ML engineers, platform teams, and research engineers who aim to self-host, fine-tune, and govern Mistral and Devstral models within production environments.
Upon completing this training, participants will be able to:
- Set up and configure self-hosted environments for Mistral and Devstral models.
- Apply fine-tuning techniques to enhance domain-specific performance.
- Implement versioning, monitoring, and lifecycle governance.
- Ensure security, compliance, and responsible usage of open-source models.
Course Format
- Interactive lectures and discussions.
- Hands-on exercises focused on self-hosting and fine-tuning.
- Live-lab implementation of governance and monitoring pipelines.
Course Customization Options
- To request customized training for this course, please contact us to arrange details.
LangGraph Applications in Finance
35 HoursLangGraph serves as a framework designed for constructing stateful, multi-agent Large Language Model applications through composable graphs that maintain persistent state and provide granular control over execution flows.
This instructor-led live training, available both online and on-site, targets intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based solutions within the finance sector, ensuring robust governance, observability, and regulatory compliance.
Upon completion of this training, participants will be capable of:
- Developing finance-specific LangGraph workflows that align with regulatory standards and audit requirements.
- Integrating financial data standards and ontologies into graph states and associated tooling.
- Establishing reliability, safety mechanisms, and human-in-the-loop controls for critical operational processes.
- Deploying, monitoring, and optimizing LangGraph systems to enhance performance, manage costs, and meet Service Level Agreements (SLAs).
Course Format
- Interactive lectures and group discussions.
- Extensive exercises and practical applications.
- Hands-on implementation within a live laboratory environment.
Customization Options
- For customized training arrangements, please reach out to our team.
LangGraph Foundations: Graph-Based LLM Prompting and Chaining
14 HoursLangGraph serves as a framework designed for constructing LLM applications with graph structures, enabling capabilities such as planning, branching, tool utilization, memory management, and controllable execution.
This instructor-led live training, available either online or on-site, targets beginner-level developers, prompt engineers, and data practitioners aiming to design and implement reliable, multi-step LLM workflows using LangGraph.
Upon completing this training, participants will be capable of:
- Explaining fundamental LangGraph concepts (nodes, edges, and state) and identifying appropriate use cases for each.
- Constructing prompt chains that feature branching logic, tool invocation, and persistent memory.
- Integrating retrieval mechanisms and external APIs into graph-based workflows.
- Testing, debugging, and evaluating LangGraph applications to ensure reliability and safety.
Course Format
- Interactive lectures accompanied by facilitated discussions.
- Guided laboratory exercises and code walkthroughs conducted within a sandbox environment.
- Scenario-based practical exercises focusing on design, testing, and evaluation.
Customization Options
- To request customized training for this course, please reach out to us to arrange your session.
LangGraph in Healthcare: Workflow Orchestration for Regulated Environments
35 HoursLangGraph facilitates stateful, multi-actor workflows driven by LLMs, offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are essential for ensuring compliance, enabling interoperability, and developing decision-support systems that align with medical processes.
This instructor-led training, available online or onsite, targets intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based healthcare solutions while navigating regulatory, ethical, and operational challenges.
Upon completion of this training, participants will be able to:
- Design healthcare-specific LangGraph workflows that prioritize compliance and auditability.
- Integrate LangGraph applications with medical ontologies and standards (FHIR, SNOMED CT, ICD).
- Apply best practices for reliability, traceability, and explainability in sensitive environments.
- Deploy, monitor, and validate LangGraph applications in healthcare production settings.
Course Format
- Interactive lectures and discussions.
- Hands-on exercises featuring real-world case studies.
- Implementation practice within 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 designed for constructing stateful, multi-actor LLM applications through composable graphs that offer persistent state and precise control over execution.
This instructor-led, live training (available online or onsite) is targeted at intermediate to advanced professionals seeking to design, implement, and operate LangGraph-based legal solutions that meet necessary compliance, traceability, and governance standards.
Upon completion of this training, participants will be able to:
- Design LangGraph workflows specific to legal needs that maintain auditability and compliance.
- Integrate legal ontologies and document standards into graph state and processing workflows.
- Implement guardrails, human-in-the-loop approvals, and traceable decision paths.
- Deploy, monitor, and maintain LangGraph services in production environments with observability and cost controls.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical sessions.
- Hands-on implementation within a live lab environment.
Customization Options
- To request customized training for this course, please contact us to arrange.
Building Dynamic Workflows with LangGraph and LLM Agents
14 HoursLangGraph serves as a framework designed for assembling graph-structured workflows involving Large Language Models (LLMs), enabling capabilities such as branching, tool utilization, memory management, and controlled execution.
This instructor-led, live training session, available either online or onsite, targets intermediate engineers and product teams seeking to merge LangGraph's graph logic with LLM agent loops. The goal is to develop dynamic, context-aware applications, including customer support agents, decision trees, and information retrieval systems.
Upon completing this training, participants will be equipped to:
- Design graph-based workflows that effectively coordinate LLM agents, tools, and memory.
- Implement conditional routing, retry mechanisms, and fallback strategies to ensure robust execution.
- Integrate retrieval processes, APIs, and structured outputs into agent loops.
- Evaluate, monitor, and enhance agent behavior to guarantee reliability and safety.
Course Format
- Interactive lectures accompanied by facilitated discussions.
- Guided laboratory exercises and code walkthroughs within a sandbox environment.
- Scenario-based design exercises and peer review sessions.
Course Customization Options
- To request 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 offers a secure, customizable, and governed conversational AI solution tailored for organizations, featuring support for RBAC, SSO, connectors, and enterprise app integrations.
This instructor-led, live training (online or onsite) targets intermediate-level product managers, IT leads, solution engineers, and security/compliance teams aiming to deploy, configure, and govern Le Chat Enterprise in enterprise environments.
By the end of this training, participants will be able to:
- Set up and configure Le Chat Enterprise for secure deployments.
- Enable RBAC, SSO, and compliance-driven controls.
- Integrate Le Chat with enterprise applications and data stores.
- Design and implement governance and admin playbooks for ChatOps.
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.
Cost-Effective LLM Architectures: Mistral at Scale (Performance / Cost Engineering)
14 HoursMistral is a family of high-performance large language models specifically designed for cost-effective production deployment at scale.
This instructor-led live training, available online or onsite, targets advanced infrastructure engineers, cloud architects, and MLOps leaders who aim to design, deploy, and optimize Mistral-based architectures to achieve maximum throughput while minimizing costs.
Upon completing this training, participants will be capable of:
- Implementing scalable deployment patterns for Mistral Medium 3.
- Applying batching, quantization, and efficient serving strategies.
- Optimizing inference costs without compromising performance.
- Designing production-ready serving topologies for enterprise workloads.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation within a live-lab environment.
Course Customization Options
- To request customized training for this course, please contact us to arrange.
Productizing Conversational Assistants with Mistral Connectors & Integrations
14 HoursMistral AI provides an open platform that empowers teams to build and incorporate conversational assistants into both enterprise internal processes and customer-facing workflows.
This instructor-led live training, available either online or onsite, targets beginner to intermediate-level product managers, full-stack developers, and integration engineers who aim to design, integrate, and productize conversational assistants utilizing Mistral connectors and integrations.
Upon completing this training, participants will be equipped to:
- Connect Mistral conversational models with enterprise and SaaS connectors.
- Implement retrieval-augmented generation (RAG) to deliver grounded responses.
- Create UX patterns for both internal and external chat assistants.
- Deploy assistants into product workflows for practical, real-world applications.
Course Format
- Interactive lectures and discussions.
- Practical integration exercises.
- Live lab sessions focused on developing 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 engineered for robust, production-ready deployment within enterprise settings.
This instructor-led training, available both online and onsite, targets intermediate to advanced AI/ML engineers, platform architects, and MLOps professionals seeking to deploy, optimize, and secure Mistral Medium 3 for business applications.
Upon completion, participants will be equipped to:
- Deploy Mistral Medium 3 via APIs or self-hosted solutions.
- Enhance inference efficiency and manage associated costs.
- Execute multimodal applications using Mistral Medium 3.
- Adopt security and compliance standards suited for enterprise environments.
Course Format
- Engaging lectures and interactive discussions.
- Extensive exercises and practical drills.
- Hands-on implementation in a live laboratory setting.
Customization Options
- For tailored training solutions, please reach out to us to coordinate.
Mistral for Responsible AI: Privacy, Data Residency & Enterprise Controls
14 HoursMistral AI offers an open, enterprise-grade AI platform equipped with features designed to support secure, compliant, and responsible AI deployment.
This instructor-led live training, available both online and onsite, targets intermediate-level compliance leaders, security architects, and legal/operations stakeholders who aim to adopt responsible AI practices using Mistral through robust privacy, data residency, and enterprise control mechanisms.
Upon completion of this training, participants will be able to:
- Deploy privacy-preserving techniques within Mistral environments.
- Utilize data residency strategies to satisfy regulatory obligations.
- Configure enterprise-grade controls, including RBAC, SSO, and audit logging.
- Assess vendor and deployment options to ensure compliance alignment.
Course Format
- Interactive lectures and discussions.
- Compliance-focused case studies and practical exercises.
- Hands-on implementation of enterprise AI controls.
Customization Options
- For customized training arrangements, please contact us.
Multimodal Applications with Mistral Models (Vision, OCR, & Document Understanding)
14 HoursMistral models are open-source artificial intelligence technologies that have expanded into multimodal workflows, supporting both language processing and vision tasks for enterprise and research initiatives.
This instructor-led, live training (available online or on-site) is designed for intermediate-level ML researchers, applied engineers, and product teams who aim to construct multimodal applications using Mistral models, specifically focusing on OCR and document analysis pipelines.
Upon completion of this training, participants will be able to:
- Set up and configure Mistral models for multimodal tasks.
- Implement OCR workflows and integrate them with NLP pipelines.
- Design document analysis applications for enterprise use cases.
- Develop vision-text search and assistive UI functionalities.
Course Format
- Interactive lecture and discussion.
- Hands-on coding exercises.
- Live-lab implementation of multimodal pipelines.
Customization Options
- To request a customized training for this course, please contact us to arrange.
