Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Training Course
Parameter-Efficient Fine-Tuning (PEFT) refers to a suite of techniques designed to efficiently adapt large language models (LLMs) by updating only a small subset of their parameters.
This instructor-led, live training (available online or on-site) is tailored for intermediate-level data scientists and AI engineers seeking to fine-tune large language models in a more cost-effective and efficient manner using methods such as LoRA, Adapter Tuning, and Prefix Tuning.
By the conclusion of this training, participants will be able to:
- Grasp the theoretical foundations of parameter-efficient fine-tuning approaches.
- Implement LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT.
- Compare the performance and cost trade-offs between PEFT methods and full fine-tuning.
- Deploy and scale fine-tuned LLMs with significantly reduced computational and storage demands.
Course Format
- Interactive lectures and group discussions.
- Abundant exercises and practical activities.
- Hands-on implementation within a live-lab environment.
Course Customisation Options
- To request a customised version of this course, please contact us to make arrangements.
Course Outline
Introduction to Parameter-Efficient Fine-Tuning (PEFT)
- Motivation and limitations of full fine-tuning
- Overview of PEFT: objectives and benefits
- Industry applications and use cases
LoRA (Low-Rank Adaptation)
- Concept and intuition underpinning LoRA
- Implementing LoRA using Hugging Face and PyTorch
- Hands-on: Fine-tuning a model with LoRA
Adapter Tuning
- How adapter modules operate
- Integration with transformer-based models
- Hands-on: Applying Adapter Tuning to a transformer model
Prefix Tuning
- Leveraging soft prompts for fine-tuning
- Strengths and limitations compared to LoRA and adapters
- Hands-on: Prefix Tuning on an LLM task
Evaluating and Comparing PEFT Methods
- Metrics for assessing performance and efficiency
- Trade-offs in training speed, memory usage, and accuracy
- Benchmarking experiments and interpretation of results
Deploying Fine-Tuned Models
- Saving and loading fine-tuned models
- Deployment considerations for PEFT-based models
- Integration into applications and data pipelines
Best Practices and Extensions
- Combining PEFT with quantisation and distillation
- Application in low-resource and multilingual contexts
- Future directions and active research areas
Summary and Next Steps
Requirements
- A solid understanding of machine learning fundamentals
- Practical experience working with large language models (LLMs)
- Proficiency in Python and PyTorch
Audience
- Data scientists
- AI engineers
Open Training Courses require 5+ participants.
Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Training Course - Booking
Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Training Course - Enquiry
Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs - Consultancy Enquiry
Provisional Upcoming Courses (Require 5+ participants)
Related Courses
Advanced LangGraph: Optimization, Debugging, and Monitoring Complex Graphs
35 HoursLangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.
This instructor-led, live training (available online or on-site) is designed for advanced-level AI platform engineers, AI-focused DevOps professionals, and ML architects who aim to optimise, debug, monitor, and operate production-grade LangGraph systems.
By the end of this training, participants will be able to:
- Design and optimise complex LangGraph topologies for speed, cost-efficiency, and scalability.
- Engineer 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.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and hands-on practice.
- Real-world implementation in a live-lab environment.
Course Customisation Options
- To request a customised training session 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 for building and running coding agents that can interact with codebases, developer tools, and APIs to enhance engineering productivity.
This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level ML engineers, developer-tooling teams, and SREs who wish to design, implement, and optimise coding agents using Devstral.
By the end of this training, participants will be able to:
- Set up and configure Devstral for coding agent development.
- Design agentic workflows for codebase exploration and modification.
- Integrate coding agents with developer tools and APIs.
- Implement best practices for secure and efficient agent deployment.
Course Format
- Interactive lectures and discussions.
- Abundant exercises and practice opportunities.
- Hands-on implementation within a live-lab environment.
Course Customisation Options
- To request a customised training session 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–level to advanced–level ML 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.
Course Format
- Interactive lecture and discussion.
- Hands-on exercises in self-hosting and fine-tuning.
- Live-lab implementation of governance and monitoring pipelines.
Course Customisation Options
- To request a customised training session for this course, please contact us to arrange.
LangGraph Applications in Finance
35 HoursLangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish 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:
- Design finance-specific LangGraph workflows aligned to regulatory and audit requirements.
- Integrate 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 optimise LangGraph systems for performance, cost, and SLAs.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customisation Options
- To request a customised training for this course, please contact us to arrange.
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.
Course Format
- Interactive lecture and facilitated discussion.
- Guided labs and code walkthroughs in a sandbox environment.
- Scenario-based exercises on design, testing, and evaluation.
Course Customisation Options
- To request a customised training for this course, please contact us to arrange.
LangGraph in Healthcare: Workflow Orchestration for Regulated Environments
35 HoursLangGraph enables stateful, multi-actor workflows powered by large language models (LLMs), offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are vital for ensuring compliance, interoperability, and the development of decision-support systems that align seamlessly with clinical workflows.
This instructor-led, live training (available online or on-site) is designed for intermediate to advanced-level professionals who wish to design, implement, and manage LangGraph-based healthcare solutions while navigating regulatory, ethical, and operational challenges.
By the end of this training, participants will be able to:
- Design healthcare-specific LangGraph workflows with compliance and auditability as core considerations.
- Integrate LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
- Apply best practices for reliability, traceability, and explainability within sensitive healthcare environments.
- Deploy, monitor, and validate LangGraph applications in real-world healthcare production settings.
Course Format
- Interactive lectures and group discussions.
- Hands-on exercises featuring real-world case studies.
- Practical implementation in a live-lab environment.
Course Customisation Options
- To request a customised version of this training, please contact us to arrange a tailored session.
LangGraph for Legal Applications
35 HoursLangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and precise control over execution.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and operate LangGraph-based legal solutions with the necessary compliance, traceability, and governance controls.
By the end of this training, participants will be able to:
- Design legal-specific LangGraph workflows that preserve 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.
- Plenty of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customisation Options
- To request a customised 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 on-site) 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 behaviour 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 Customisation Options
- To request customised 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 personalising content pipelines.
This instructor-led, live training (online or on-site) 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 personalisation.
- Manage state, memory, and context across multi-step campaigns.
- Evaluate, monitor, and optimise 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 personalisation, segmentation, and branching logic.
Course Customisation Options
- To request a customised 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 delivering secure, customisable, and governed conversational AI capabilities for organisations, with support for RBAC, SSO, connectors, and enterprise application integrations.
This instructor-led, live training (available online or on-site) is designed for intermediate-level product managers, IT leads, solution engineers, and security and compliance teams who wish to deploy, configure, and govern Le Chat Enterprise within 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.
Course Format
- Interactive lecture and discussion.
- Extensive exercises and practice opportunities.
- Hands-on implementation in a live-lab environment.
Course Customisation Options
- To request a customised training session for this course, 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 optimised for cost-effective production deployment at scale.
This instructor-led, live training (online or on-site) is designed for advanced-level infrastructure engineers, cloud architects, and MLOps leads who wish to design, deploy, and optimise Mistral-based architectures to achieve 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, quantisation, and efficient serving strategies.
- Optimise inference costs while maintaining performance.
- Design production-ready serving topologies for enterprise workloads.
Format of the Course
- Interactive lecture and discussion.
- Abundant exercises and hands-on practice.
- Live implementation in a real-time lab environment.
Course Customisation Options
- To request a customised training session for this course, please contact us to arrange.
Productizing Conversational Assistants with Mistral Connectors & Integrations
14 HoursMistral AI is an open AI platform that enables teams to build and integrate conversational assistants into enterprise and customer-facing workflows.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level product managers, full-stack developers, and integration engineers who wish to design, integrate, and productise conversational assistants using Mistral connectors and integrations.
By the end of this training, participants will be able to:
- Integrate Mistral conversational models with enterprise and SaaS connectors.
- Implement retrieval-augmented generation (RAG) for grounded responses.
- Design UX patterns for internal and external chat assistants.
- Deploy assistants into product workflows for real-world use cases.
Format of the Course
- Interactive lecture and discussion.
- Hands-on integration exercises.
- Live-lab development of conversational assistants.
Course Customisation Options
- To request a customised 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 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, optimise, 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.
- Optimise inference performance and costs.
- Implement multimodal use cases with Mistral Medium 3.
- Apply security and compliance best practices for enterprise environments.
Course Format
- Interactive lectures and discussions.
- Extensive hands-on exercises and practice.
- Real-world implementation in a live-lab environment.
Course Customisation Options
- To request a customised training session for this course, please contact us to make arrangements.
Mistral for Responsible AI: Privacy, Data Residency & Enterprise Controls
14 HoursMistral AI is an open, enterprise-ready AI platform delivering features that enable secure, compliant, and responsible AI deployment.
This instructor-led, live training (available online or on-site) is designed for intermediate-level compliance leads, security architects, and legal and operations stakeholders seeking to implement responsible AI practices with Mistral by leveraging privacy safeguards, data residency strategies, and enterprise-grade control mechanisms.
By the end of this training, participants will be able to:
- Implement privacy-preserving techniques within Mistral deployments.
- Apply data residency strategies to meet regulatory requirements.
- Configure enterprise-grade controls such as role-based access control (RBAC), single sign-on (SSO), and audit logging.
- Evaluate vendor and deployment options to ensure compliance alignment.
Course Format
- Interactive lectures and group discussions.
- Compliance-focused case studies and practical exercises.
- Hands-on implementation of enterprise AI controls.
Course Customisation Options
- To request a customised version of this training, please contact us to make arrangements.
Multimodal Applications with Mistral Models (Vision, OCR, & Document Understanding)
14 HoursMistral models are open-source AI technologies that now extend into multimodal workflows, supporting both language and vision tasks for enterprise and research applications.
This instructor-led, live training (delivered online or on-site) is designed for intermediate-level ML researchers, applied engineers, and product teams who wish to build 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 NLP pipelines.
- Design document understanding applications for enterprise use cases.
- Develop vision-text search and assistive UI functionalities.
Course Format
- Interactive lectures and discussions.
- Hands-on coding exercises.
- Live-lab implementation of multimodal pipelines.
Course Customisation Options
- To request a customised training session for this course, please contact us to arrange.
