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Learning by doing
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Trainers with practical experience
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Classroom training
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Detailed course material
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Clear content description
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Tailormade content possible
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Training that proceeds
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Small groups
Architecture, training, optimization, and deployment of large language models.
Course Overview
This 2-day hands-on course provides an in-depth understanding of GitLab CI/CD, a powerful tool for automating software builds, testing, and deployments. Participants will learn how to set up, configure, and optimize CI/CD pipelines in GitLab to enhance software delivery efficiency.
By the end of the course, participants will be able to:
• Understand GitLab CI/CD architecture and concepts
• Create CI/CD pipelines using .gitlab-ci.yml
• Implement continuous integration and delivery best practices
• Automate builds, testing, and deployments
• Integrate GitLab CI/CD with Docker, Kubernetes, and cloud platforms
• Implement security and compliance checks in pipelines
The course Large Language Models is intended for software engineers, data scientists, and technical professionals who want to work with large language models (LLMs).
To participate in the course, a basic understanding of Python and machine learning is required. Familiarity with neural networks or natural language processing is useful.
The course is led by an experienced trainer and includes a mix of theory and hands-on exercises. Demonstrations and case studies involving LLMs are used to illustrate key concepts.
After successfully completing the course, attendants receive a certificate of participation in the course Large Language Models.
Module 1: Intro to LLMs |
Module 2: Model Architectures |
Module 3: Training LLMs |
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What are LLMs? Transformer architecture Training Objectives (causal, masked) Evolution of LLMs (GPT, BERT, T5) Open Source vs Proprietary LLMs Tokenization and Vocabulary Attention Mechanism Model Scaling Laws Transfer Learning Pretraining vs Fine-Tuning |
Decoder vs Encoder-Decoder Models GPT, LLaMA, T5, and PaLM Training Pipeline Overview Optimizers (Adam, Adafactor) Precision (FP32, FP16, quantization) Transformers (HF), Megatron, Deepspeed Parameter vs Instruction Suning LoRA and QLoRA In-context Learning Reinforcement Learning with HF |
Dataset Creation and Curation Tokenizer Customization Data Preprocessing Fine-Tuning with Hugging Face SFT (Supervised Fine-Tuning) Adapters and LoRA Evaluation Metrics Avoiding Overfitting Model Alignment Model Evaluation and Benchmarking |
Module 4: LLM Deployment |
Module 5: Safety and Bias |
Module 6: LLM Use Cases |
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Inference Optimization Model Distillation Quantization Techniques Hosting on AWS, GCP, Azure Using Model Gateways LangChain and Semantic Search Vector Stores and Embeddings Caching Responses Load Balancing Cost Optimization Strategies |
Understanding Model Biases Mitigation Strategies Model Auditing Adversarial Prompts User Privacy Filtering and Moderation Red Teaming Explainability in LLMs Interpreting Outputs Regulatory and Legal Issues |
Coding Assistants AI for Legal and Finance Education and Learning Health Care and Biotech Chatbots and Agents RAG Systems Tool Use and Plugins Enterprise Use of LLMs Evaluating New Models Future Directions LLM Research |
These companies already made use of our courses:
These companies used our courses:
