The Large Language Model Course

This comprehensive course explores Large Language Models (LLMs), offering two distinct learning paths: the LLM Scientist track for building optimal LLMs, and the LLM Engineer track for developing and deploying LLM-based applications. An interactive version is available via HuggingChat (recommended) or ChatGPT.

LLM Scientist Track: This path focuses on mastering LLM construction using cutting-edge techniques.

1. LLM Architecture: While deep Transformer architecture knowledge isn't mandatory, understanding core processes is crucial: text-to-number conversion (tokenization), token processing through attention mechanisms, and text generation via various sampling strategies. Key areas include understanding architectural evolution, tokenization strategies, attention mechanisms (self-attention and variants), and text generation sampling techniques.

2. Pre-training Models: Although computationally intensive, grasping pre-training is vital. This involves data preparation (curation, cleaning, deduplication), distributed training (data, pipeline, and tensor parallelism), training optimization (adaptive learning rates, gradient clipping), and meticulous monitoring of key metrics.

3. Post-training Datasets: Post-training datasets, structured with instructions and answers (supervised fine-tuning) or preference pairs (preference alignment), are crucial. This section covers storage formats, synthetic data generation, data enhancement techniques, and robust quality filtering methods.

4. Supervised Fine-Tuning (SFT): SFT transforms base models into helpful assistants. This involves understanding training techniques (full fine-tuning vs. parameter-efficient methods like LoRA and QLoRA), key training parameters, distributed training strategies, and effective monitoring techniques.

5. Preference Alignment: This stage aligns model outputs with human preferences, mitigating toxicity and hallucinations. It focuses on rejection sampling, Direct Preference Optimization (DPO), Proximal Policy Optimization (PPO), and performance monitoring.

6. Evaluation: Reliable LLM evaluation is crucial. This section explores automated benchmarks, human evaluation, model-based evaluation, and the importance of analyzing feedback signals to improve model performance.

7. Quantization: This section delves into techniques for reducing the computational and memory costs of LLMs through parameter and activation quantization, covering various precision levels, tools like llama.cpp and GGUF, and advanced techniques like GPTQ, AWQ, SmoothQuant, and ZeroQuant.

8. Emerging Trends: This section explores advanced topics like model merging, multimodal models, interpretability techniques (including abliteration), and test-time compute scaling.

LLM Engineer Track: This path focuses on building production-ready LLM applications, emphasizing model augmentation and deployment.

1. Running LLMs: This section covers running LLMs via APIs (both private and open-source), utilizing open-source LLMs from the Hugging Face Hub, mastering prompt engineering techniques, and structuring outputs for specific applications.

2. Building a Vector Storage: This section details creating a vector storage for Retrieval Augmented Generation (RAG) pipelines, including document ingestion, splitting, embedding models, and vector databases.

3. Retrieval Augmented Generation (RAG): This section explains how to build RAG pipelines, focusing on orchestrators, retrievers, memory management, and evaluation metrics.

4. Advanced RAG: This section covers advanced RAG techniques like query construction for structured databases, agent-based systems, post-processing methods, and programmatic LLM optimization using frameworks like DSPy.

5. Inference Optimization: This section explores techniques for optimizing LLM inference, including Flash Attention, key-value caching, and speculative decoding.

6. Deploying LLMs: This section covers local deployment options, creating demos using frameworks like Gradio and Streamlit, server-side deployment strategies, and edge deployment for constrained environments.

7. Securing LLMs: This section addresses the unique security challenges posed by LLMs, including prompt hacking, backdoors, and defensive measures.

Conclusion: The course encourages hands-on learning and exploration, suggesting experimentation with various tools and techniques to develop expertise in a chosen area within the vast LLM landscape.

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