Fine-Tuning Large Language Models Using QLoRA Training Course
QLoRA is an advanced method for fine-tuning large language models (LLMs) by utilizing quantization techniques. This approach provides a more efficient way to refine these models without the need for significant computational resources. The training will delve into both the theoretical underpinnings and practical applications of using QLoRA for fine-tuning LLMs.
This instructor-led, live training (available online or on-site) is designed for intermediate to advanced machine learning engineers, AI developers, and data scientists who want to learn how to effectively use QLoRA to fine-tune large models for specific tasks and customizations.
By the end of this training, participants will be able to:
- Grasp the theory behind QLoRA and quantization methods for LLMs.
- Apply QLoRA in the process of fine-tuning large language models for domain-specific purposes.
- Enhance fine-tuning performance on limited computational resources through quantization.
- Efficiently deploy and assess fine-tuned models in real-world scenarios.
Format of the Course
- Interactive lectures and discussions.
- A wealth of exercises and practical activities.
- Hands-on implementation in a live-lab setting.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
- An understanding of machine learning fundamentals and neural networks
- 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 is a framework designed for constructing stateful, multi-actor LLM applications using composable graphs that maintain persistent state and provide control over execution.
This instructor-led, live training (available online or on-site) targets advanced-level AI platform engineers, DevOps professionals specializing in AI, 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 improved speed, cost efficiency, and scalability.
- Ensure reliability through the implementation of retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
- Effectively debug and trace graph executions, inspect state, and systematically replicate production issues.
- Instrument graphs with logs, metrics, and traces, deploy them to production, and monitor service level agreements (SLAs) and costs.
Format of the Course
- Interactive lectures and discussions.
- Plenty of exercises and practical activities.
- 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 designed to build and run coding agents that can interact with codebases, developer tools, and APIs, thereby enhancing engineering productivity.
This instructor-led, live training (available both online and on-site) targets intermediate to advanced ML engineers, developer-tooling teams, and SREs who wish to design, implement, and optimize coding agents using Devstral.
By the end of this training, participants will be able to:
- Set up and configure Devstral for developing coding agents.
- Create agentic workflows for exploring and modifying codebases.
- Integrate coding agents with developer tools and APIs.
- Implement best practices for secure and efficient agent deployment.
Format of the Course
- Interactive lectures and discussions.
- Plenty of exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a 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 (online or on-site) is aimed at intermediate to advanced ML engineers, platform teams, and research engineers who want to self-host, fine-tune, and manage 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 to enhance performance for specific domains.
- Implement versioning, monitoring, and lifecycle management practices.
- Ensure the security, compliance, and responsible use of open-source models.
Format of the Course
- Interactive lectures and discussions.
- 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 is a framework designed for creating stateful, multi-actor LLM applications through composable graphs that maintain persistent state and provide control over execution.
This instructor-led, live training (available online or onsite) is targeted at intermediate to advanced professionals who aim to design, implement, and operate LangGraph-based finance solutions with proper governance, observability, and compliance.
By the end of this training, participants will be able to:
- Create finance-specific LangGraph workflows that align with regulatory and audit requirements.
- Incorporate financial data standards and ontologies into graph state and tooling.
- Implement reliability, safety, and human-in-the-loop controls for critical processes.
- Deploy, monitor, and optimize LangGraph systems to ensure performance, cost efficiency, and SLA compliance.
Format of the Course
- Interactive lectures and discussions.
- Plenty of exercises and practical activities.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
LangGraph Foundations: Graph-Based LLM Prompting and Chaining
14 HoursLangGraph is a framework designed for developing graph-structured LLM applications that support planning, branching, tool integration, memory management, and controllable execution.
This instructor-led, live training (available online or onsite) is tailored for beginner-level developers, prompt engineers, and data professionals who want to design and build reliable, multi-step LLM workflows using LangGraph.
By the end of this training, participants will be able to:
- Understand key LangGraph concepts (nodes, edges, state) and know when to apply them.
- Create prompt chains that branch, utilize tools, and retain memory.
- Incorporate retrieval mechanisms and external APIs into graph workflows.
- Test, debug, and assess LangGraph applications for reliability and safety.
Format of the Course
- Interactive lectures and facilitated discussions.
- Guided labs and code walkthroughs in a sandbox environment.
- Scenario-based exercises focused 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 enables 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, interoperability, and the development of decision-support systems that align with medical workflows.
This instructor-led, live training (available online or on-site) is designed for intermediate to advanced professionals who want to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.
By the end of this training, participants will be able to:
- Create healthcare-specific LangGraph workflows with a focus on compliance and auditability.
- Integrate LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
- Implement best practices for reliability, traceability, and explainability in sensitive environments.
- Deploy, monitor, and validate LangGraph applications in healthcare production settings.
Format of the Course
- Interactive lectures and discussions.
- Hands-on exercises with real-world case studies.
- Practical implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
LangGraph for Legal Applications
35 HoursLangGraph is a framework designed for creating stateful, multi-actor LLM applications using composable graphs that maintain persistent state and offer precise control over execution.
This instructor-led, live training (available both online and on-site) is tailored for intermediate to advanced professionals who aim to design, implement, and operate LangGraph-based legal solutions with the required 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 lectures and discussions.
- Numerous exercises and practice sessions.
- 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 designed for creating graph-structured workflows with LLMs (Language Models), supporting features such as branching, tool integration, memory management, and controlled execution.
This instructor-led, live training (available both online and on-site) is targeted at intermediate-level engineers and product teams who aim to integrate LangGraph’s graph logic with LLM agent loops to develop dynamic, context-aware applications like customer support agents, decision trees, and information retrieval systems.
By the end of this training, participants will be able to:
- Design workflows based on graphs that coordinate LLM agents, tools, and memory effectively.
- Implement conditional routing, retries, and fallback mechanisms to ensure robust execution.
- Integrate retrieval systems, APIs, and structured outputs into agent loops seamlessly.
- Evaluate, monitor, and enhance the reliability and safety of agent behavior.
Format of the Course
- Interactive lectures and facilitated discussions.
- Guided labs and code walkthroughs in a secure sandbox environment.
- Scenario-based design exercises and peer reviews to reinforce learning.
Course Customization Options
- To request a customized training for this course, please contact us to arrange the details.
LangGraph for Marketing Automation
14 HoursLangGraph is a graph-based orchestration framework that enables the creation of conditional, multi-step workflows involving LLMs and tools. It is particularly useful for automating and personalizing content pipelines.
This instructor-led, live training (available both online and onsite) is designed for intermediate-level marketers, content strategists, and automation developers who want 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 that incorporate conditional logic.
- Integrate LLMs, APIs, and data sources to achieve automated personalization.
- Manage state, memory, and context throughout multi-step campaigns.
- Assess, monitor, and optimize the performance and delivery outcomes of workflows.
Format of the Course
- Interactive lectures and group discussions.
- Hands-on labs for implementing email workflows and content pipelines.
- Scenario-based exercises focused 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 is a private ChatOps solution that offers secure, customizable, and governed conversational AI capabilities for organizations. It supports role-based access control (RBAC), single sign-on (SSO), connectors, and integrations with enterprise applications.
This instructor-led, live training (available online or on-site) is designed for intermediate-level product managers, IT leads, solution engineers, and security/compliance teams who aim to deploy, configure, and manage 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.
- Extensive exercises and practice sessions.
- 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 high-performance series of large language models designed for cost-effective deployment at scale.
This instructor-led, live training (available online or onsite) is targeted at advanced-level infrastructure engineers, cloud architects, and MLOps leads who aim to design, deploy, and optimize Mistral-based architectures for optimal throughput and minimal costs.
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.
- Extensive exercises and practice sessions.
- 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 is an open artificial intelligence platform that empowers teams to develop and integrate conversational assistants into both enterprise and customer-facing workflows.
This instructor-led, live training (available online or on-site) is designed for product managers, full-stack developers, and integration engineers at beginner to intermediate levels who are interested in designing, integrating, and productizing conversational assistants using Mistral's connectors and integrations.
By the end of this training, participants will be able to:
- Integrate Mistral's conversational models with enterprise and SaaS connectors.
- Implement retrieval-augmented generation (RAG) for more grounded and contextually relevant responses.
- Create user experience (UX) patterns for both internal and external chat assistants.
- Deploy these assistants into real-world product workflows.
Format of the Course
- Interactive lectures and discussions.
- Practical integration exercises.
- Live-lab development of conversational assistants.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Enterprise-Grade Deployments with Mistral Medium 3
14 HoursMistral Medium 3 is a high-performance, multimodal large language model designed for robust deployment across enterprise environments.
This instructor-led, live training (available online or on-site) is tailored for intermediate to advanced AI/ML engineers, platform architects, and MLOps teams who aim to deploy, optimize, and secure Mistral Medium 3 for enterprise applications.
By the end of this training, participants will be able to:
- Deploy Mistral Medium 3 using both API and self-hosted methods.
- Optimize inference performance and manage costs effectively.
- Implement multimodal use cases with Mistral Medium 3.
- Apply best practices for security and compliance in enterprise settings.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and practical sessions.
- 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 is an open and enterprise-ready artificial intelligence platform that offers features for secure, compliant, and responsible AI deployment.
This instructor-led, live training (available online or onsite) is designed for intermediate-level compliance leads, security architects, and legal/operations stakeholders who want to implement responsible AI practices using Mistral. The focus will be on leveraging privacy, data residency, and enterprise control mechanisms.
By the end of this training, participants will be able to:
- Implement privacy-preserving techniques in their Mistral deployments.
- Apply data residency strategies to comply with regulatory requirements.
- Set up advanced enterprise controls such as Role-Based Access Control (RBAC), Single Sign-On (SSO), and audit logs.
- Evaluate different vendor and deployment options to ensure compliance alignment.
Format of the Course
- Interactive lectures and discussions.
- Compliance-focused case studies and exercises.
- Practical hands-on implementation of enterprise AI controls.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Multimodal Applications with Mistral Models (Vision, OCR, & Document Understanding)
14 HoursMistral models are open-source AI technologies that now support multimodal workflows, enabling both language and vision tasks for enterprise and research applications.
This instructor-led, live training (available online or onsite) is designed for intermediate-level ML researchers, applied engineers, and product teams who want to develop multimodal applications using Mistral models, including OCR and document understanding pipelines.
By the end of this training, participants will be able to:
- Set up and configure Mistral models for multimodal tasks.
- Implement OCR workflows and integrate them with natural language processing (NLP) pipelines.
- Design document understanding applications tailored for enterprise use cases.
- Develop vision-text search and assistive user interface functionalities.
Format of the Course
- Interactive lectures and discussions.
- Hands-on coding exercises.
- Live-lab implementation of multimodal pipelines.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
