1.
Deep Dive into Prompt Engineering | LLM Advanced
Training Coverage:
Definition and purpose of prompt engineering
- Various prompting techniques (program-aided, k-shot, iterative, chain-of-thought)
General tips for designing effective prompts
- Complexity of prompt engineering
- Importance of experimentation
- Adapting techniques for different tasks
2. Use Cases using LLMs (RAG) | LLM Advanced
Training Coverage:
Vector Database: Introduction to vector databases, their role in managing high-dimensional
data, and applications in Al, especially in retrieving and indexing large-scale embeddings.
- Retrieval-Augmented Generation (RAG): Overview of RAG models, which combine retrieval
mechanisms with generative models to improve the relevance and accuracy of responses by
integrating external data sources.
- LLamaindex & Langchains: Introduction to LLamaindex and Langchains, exploring their
frameworks, functionalities, and how they simplify building and deploying language model
applications.
- Agents: Understanding the concept of agents in Al, their role in automating tasks,
decision-making processes, and integrating with other Al models or systems.
3. Fine Tuning | LLM Advanced
Training Coverage:
- Fine-Tuning Large Language Models (LLMS): Overview of techniques and best practices for
fine-tuning LLMs like GPT-3, including both single-task and multi-task fine-tuning.
Instruction fine-tuning to improve models' ability to follow specific instructions and perform tasks
more accurately.
Exploration of model evaluation and benchmarking methods to assess the effectiveness of
fine-tuning.
Advanced Techniques in Model Fine-Tuning: Introduction to parallelism strategies, gradient
checkpointing, and DeepSpeed for efficient fine-tuning of large models.
Case studies and research papers (e.g., Stanford's Alpaca) highlighting novel fine-tuning
approaches and imitation learning in LLMs.
- Practical Implementation: Hands-on guidance on fine-tuning GPT models using tools and
platforms like Hugging Face, as well as real-world applications and outcomes.
Understanding the challenges and solutions in model imitation, where LLMs learn tasks.
4. Fine Tuning | LLM Advanced
Training Coverage:
- Parameter-Efficient Fine-Tuning (PEFT): Overview of PEFT techniques that enable the
fine-tuning of large language models (LLMs) with fewer parameters, making the process more
efficient and cost-effective.
Focus on techniques such as LoRA (Low-Rank Adaptation), soft prompts, and LLAMA adapters
to optimize model performance with minimal resource use.
Advanced Fine-Tuning Techniques: Detailed exploration of specific PEFT methods like LoRA
and P-Tuning, including practical implementations and case studies using LLAMA and Alpaca
models.
Hands-on tutorials and walkthroughs for fine-tuning LLMs using these techniques, with a focus
on optimizing for different tasks and environments.
- Prompt Engineering: Introduction to prompt engineering, including methods like P-Tuning,
which fine-tune prompts instead of model parameters to guide LLMs' outputs more effectively.
- Practical Fine-Tuning Examples: Real-world examples and tutorials on using PEFT techniques
to fine-tune LLMS, including the setup and execution of fine-tuning processes on GPUs using
tools like QLORA
5. Research and Optimization (Advanced) | LLM Advanced
Training Coverage:
- Transformer Architecture & Scaling Laws:Detailed exploration of Transformer models, scaling
laws, pretraining, fine-tuning, and the emergence of complex abilities in large models.
Reinforcement Learning from Human Feedback (RLHF): Understanding the principles and
implementation of RLHF in fine-tuning language models, including how human feedback is used
to improve model behavior and reward modeling.
- Generative Al with LLMS:Practical walkthroughs and tutorials on implementing generative Al
techniques with large language models (LLMs), focusing on RLHF and its applications
- Advanced Model Engineering: Techniques in LLM engineering, including model quantization,
inference optimization, parameter-efficient fine-tuning (PEFT), and the development of
advanced language models like StackLLAMA.
- Practical Tutorials and Case Studies Real-world examples and hands-on sessions showcasing
the application of RLHF and LLMs in various tasks, such as human feedback loops and reward
model fine-tuning
6. Agents | LLM Advanced
Training Coverage:
-Al Agents and Architectures: Overview of Al agents, including their architectures, behaviors,
and applications in various domains.
In-depth exploration of agent types, from reactive and deliberative agents to more advanced
multi-agent systems.
Auto-GPT and Autonomous Agents: Detailed understanding of Auto-GPT, an autonomous
GPT-4 powered agent that can perform tasks with minimal human intervention.
Coverage of setup, use cases, and the underlying architecture of Auto-GPT and similar systems
like AgentGPT and BabyAGI.
anc...
Exploration of real-world applications and how these agents can be leveraged for complex task
automation.
ati...
-Hugging GPT and Task-Oriented Al: Introduction to HuggingGPT (Microsoft Jarvis), an Al
framework that integrates language models with specialized tools to solve complex tasks.
Practical knowledge on setting up, using, and integrating HuggingGPT for advanced Al tasks.
Optimizing Large Language Models (LLMs): Techniques to optimize the performance of LLMs,
including methods for speeding up processing and extending context windows
