Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Training Course
Course Outline
Overview of Parameter-Efficient Fine-Tuning (PEFT)
- Rationale and constraints associated with comprehensive model fine-tuning
- Strategic objectives and operational advantages of PEFT methodologies
- Relevant applications and sector-specific use cases for government
Low-Rank Adaptation (LoRA)
- Theoretical framework and operational logic underlying LoRA
- Implementation protocols using Hugging Face and PyTorch frameworks
- Practical exercise: Executing model fine-tuning via LoRA
Adapter Tuning
- Operational mechanics of adapter modules within model architectures
- Integration protocols for transformer-based systems
- Practical exercise: Applying Adapter Tuning to transformer models
Prefix Tuning
- Utilization of continuous inputs for fine-tuning processes
- Comparative analysis of performance and constraints relative to LoRA and adapters
- Practical exercise: Implementing Prefix Tuning for large language model tasks
Evaluation and Comparison of PEFT Methodologies
- Performance and efficiency assessment metrics
- Analysis of trade-offs among training latency, resource consumption, and accuracy
- Conducting benchmarking experiments and interpreting findings
Deployment of Fine-Tuned Models
- Procedures for saving and loading fine-tuned model weights
- Operational considerations for deploying PEFT-based models in government environments
- Integration strategies for applications and data pipelines
Best Practices and Technical Extensions
- Synthesizing PEFT with quantization and distillation techniques
- Application in low-resource and multilingual contexts
- Emerging research trends and future development pathways
Summary and Recommended Next Steps
Requirements
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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
Upcoming Courses
Related Courses
Advanced LangGraph: Optimization, Debugging, and Monitoring Complex Graphs
35 HoursBuilding Coding Agents with Devstral: From Agent Design to Tooling
14 HoursOpen-Source Model Ops: Self-Hosting, Fine-Tuning and Governance with Devstral & Mistral Models
14 HoursLangGraph Applications in Finance
35 HoursLangGraph Foundations: Graph-Based LLM Prompting and Chaining
14 HoursLangGraph in Healthcare: Workflow Orchestration for Regulated Environments
35 HoursLangGraph for Legal Applications
35 HoursBuilding Dynamic Workflows with LangGraph and LLM Agents
14 HoursLangGraph for Marketing Automation
14 HoursLe Chat Enterprise: Private ChatOps, Integrations & Admin Controls
14 HoursCost-Effective LLM Architectures: Mistral at Scale (Performance / Cost Engineering)
14 HoursProductizing Conversational Assistants with Mistral Connectors & Integrations
14 HoursMistral AI is an open artificial intelligence platform that enables teams to develop and integrate conversational assistants into enterprise and customer-facing workflows.
This instructor-led, live training (available online or on-site) is designed for beginner to intermediate level product managers, full-stack developers, and integration engineers who wish to design, integrate, and deploy conversational assistants using Mistral connectors and integrations for government applications.
By the end of this training, participants will be able to:
- Integrate Mistral conversational models with enterprise and SaaS connectors for seamless communication.
- Implement retrieval-augmented generation (RAG) to ensure responses are well-grounded and contextually relevant.
- Design user experience (UX) patterns for both internal and external chat assistants, enhancing usability and efficiency.
- Deploy conversational assistants into product workflows for practical and real-world use cases, ensuring they meet the needs of government operations.
Format of the Course
- Interactive lecture and discussion to foster understanding and engagement.
- Hands-on integration exercises to apply concepts in a practical setting.
- Live-lab development of conversational assistants to reinforce learning through real-world scenarios.
Course Customization Options
- To request a customized training for this course, tailored specifically to government needs, 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 and government environments.
This instructor-led, live training (online or onsite) is aimed at intermediate to advanced AI/ML engineers, platform architects, and MLOps teams who wish to deploy, optimize, and secure Mistral Medium 3 for government 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 and government environments.
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.