# Continuum Labs

## Continuum - Models and Applications

- [Continuum Labs - Applied AI](https://training.continuumlabs.ai/continuum-applications/continuum-labs-applied-ai.md): "Applied Artificial Intelligence"
- [What we do](https://training.continuumlabs.ai/continuum-applications/overview/what-we-do.md): Continuum AI Driven Modules
- [Our Features](https://training.continuumlabs.ai/continuum-applications/overview/our-features.md): Rapid development of customised models for immediate deployment
- [Secure and Private GPU Infrastructure](https://training.continuumlabs.ai/continuum-applications/overview/secure-and-private-gpu-infrastructure.md)
- [Generative AI Implementation Risks](https://training.continuumlabs.ai/continuum-applications/overview/generative-ai-implementation-risks.md)
- [Model Range](https://training.continuumlabs.ai/continuum-applications/model-range/model-range.md): Our range of models
- [Investment Management](https://training.continuumlabs.ai/continuum-applications/model-range/investment-management.md): A model to give all investors edge
- [Employment Law](https://training.continuumlabs.ai/continuum-applications/model-range/employment-law.md): Navigating the intricaties of Australian employment law
- [Psychology and Mental Health](https://training.continuumlabs.ai/continuum-applications/model-range/psychology-and-mental-health.md): Providing assistance to professional psychologists and mental health workers
- [Home Insurance](https://training.continuumlabs.ai/continuum-applications/model-range/home-insurance.md): Providing assistance to consumers navigating the complexity of home insurance products
- [Consumer Surveying](https://training.continuumlabs.ai/continuum-applications/model-range/consumer-surveying.md): A deeper and more nuanced interaction with consumers
- [Government Grants](https://training.continuumlabs.ai/continuum-applications/model-range/government-grants.md)
- [Aged Care](https://training.continuumlabs.ai/continuum-applications/model-range/aged-care.md): Customised Fine-Tuned Large Language Models in the Aged Care Sector: A Comprehensive Use Case
- [Pharmaceuticals Benefit Scheme](https://training.continuumlabs.ai/continuum-applications/model-range/pharmaceuticals-benefit-scheme.md)
- [Three ideas for autonomous agent applications](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/three-ideas-for-autonomous-agent-applications.md)
- [Financial Statement analysis with large language models](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/financial-statement-analysis-with-large-language-models.md): The University of Chicago, Booth School of Business
- [The Evolution of AI Agents and Their Potential for Augmenting Human Agency](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/the-evolution-of-ai-agents-and-their-potential-for-augmenting-human-agency.md)
- [Better Call Saul - SaulLM-7B - a legal large language model](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/better-call-saul-saullm-7b-a-legal-large-language-model.md)
- [MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/mentallama-interpretable-mental-health-analysis-on-social-media-with-large-language-models.md): Kailai Yang, Tianlin Zhang, Ziyan Kuang, Qianqian Xie, Jimin Huang, Sophia Ananiadou
- [Anomaly detection in logging data](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/anomaly-detection-in-logging-data.md)
- [ChatDoctor: Artificial Intelligence powered doctors](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/chatdoctor-artificial-intelligence-powered-doctors.md)
- [Navigating the Jagged Technological Frontier: Effects of AI on Knowledge Workers](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/navigating-the-jagged-technological-frontier-effects-of-ai-on-knowledge-workers.md): Harvard Business School - 2023
- [Effect of AI on the US labour market](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/effect-of-ai-on-the-us-labour-market.md)
- [Data Interpreter: An LLM Agent For Data Science](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/data-interpreter-an-llm-agent-for-data-science.md)
- [The impact of AI on the customer support industry](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/the-impact-of-ai-on-the-customer-support-industry.md): Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond
- [Can Large Language Models Reason and Plan?](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/can-large-language-models-reason-and-plan.md): The answer according to this research is 'no'
- [KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/knowagent-knowledge-augmented-planning-for-llm-based-agents.md)
- [The flaws of 'product-market fit' in an emerging industry](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/the-flaws-of-product-market-fit-in-an-emerging-industry.md): Why is it used as a term and is it relevant in a new industry like artificial intelligence?
- [Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/experimental-evidence-on-the-productivity-effects-of-generative-artificial-intelligence.md)
- [The Disruption of the Administrative Class: How Generative AI is Reshaping Organisational Operations](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/the-disruption-of-the-administrative-class-how-generative-ai-is-reshaping-organisational-operations.md)
- [How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/how-knowledge-workers-think-generative-ai-will-not-transform-their-industries.md)
- [Embracing AI: A Strategic Imperative for Modern Leadership](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/embracing-ai-a-strategic-imperative-for-modern-leadership.md)
- [Artificial Intelligence and Management: The Automation-Augmentation Paradox](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/artificial-intelligence-and-management-the-automation-augmentation-paradox.md): The complex interplay between automation and augmentation in the use of artificial intelligence (AI) in management
- [Network effects in AI models](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/network-effects-in-ai-models.md): The Role of Artificial Intelligence and Data Network Effects for Creating User Value
- [AI impact on the publishing industry](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/ai-impact-on-the-publishing-industry.md)
- [Power asymmetry](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/power-asymmetry.md)
- [Information Asymmetry](https://training.continuumlabs.ai/continuum-applications/discussion-and-use-cases/information-asymmetry.md): Applied AI can reduce this major economic inefficiency

## Continuum Knowledge

- [Continuum](https://training.continuumlabs.ai/continuum.md): Applied Artificial Intelligence
- [Datasets](https://training.continuumlabs.ai/data/datasets.md)
- [Pre Training Data](https://training.continuumlabs.ai/data/datasets/pre-training-data.md): Training Foundation Models
- [Types of Fine Tuning](https://training.continuumlabs.ai/data/datasets/types-of-fine-tuning.md): Some definitions
- [Self Instruct Paper](https://training.continuumlabs.ai/data/datasets/self-instruct-paper.md): The most highly cited paper on fine tuning methods
- [Self-Alignment with Instruction Backtranslation](https://training.continuumlabs.ai/data/datasets/self-alignment-with-instruction-backtranslation.md): Xian Li, Ping Yu, Chunting Zhou, Timo Schick, Omer Levy, Luke Zettlemoyer, Jason Weston, Mike Lewis
- [Systematic Evaluation of Instruction-Tuned Large Language Models on Open Datasets](https://training.continuumlabs.ai/data/datasets/systematic-evaluation-of-instruction-tuned-large-language-models-on-open-datasets.md)
- [Instruction Tuning](https://training.continuumlabs.ai/data/datasets/instruction-tuning.md): Inspired by the Self-Instruct Paper
- [Instruction Fine Tuning - Alpagasus](https://training.continuumlabs.ai/data/datasets/instruction-fine-tuning-alpagasus.md): "ALPAGASUS: Data-Driven Data Selection for Instruction Fine-Tuning"
- [Less is More For Alignment](https://training.continuumlabs.ai/data/datasets/less-is-more-for-alignment.md): Co-authored by researchers from Meta, Carnegie Mellon University, University of Southern California, and Tel Aviv University
- [Enhanced Supervised Fine Tuning](https://training.continuumlabs.ai/data/datasets/enhanced-supervised-fine-tuning.md): How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
- [Visualising Data using t-SNE](https://training.continuumlabs.ai/data/datasets/visualising-data-using-t-sne.md)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://training.continuumlabs.ai/data/datasets/umap-uniform-manifold-approximation-and-projection-for-dimension-reduction.md)
- [Training and Evaluation Datasets](https://training.continuumlabs.ai/data/datasets/training-and-evaluation-datasets.md)
- [What is perplexity?](https://training.continuumlabs.ai/data/datasets/what-is-perplexity.md)
- [Foundation Models](https://training.continuumlabs.ai/models/foundation-models.md): Continuum AI Driven Modules
- [The leaderboard](https://training.continuumlabs.ai/models/foundation-models/the-leaderboard.md)
- [Foundation Models](https://training.continuumlabs.ai/models/foundation-models/foundation-models.md): Training Foundation Models
- [LLama 2 - Analysis](https://training.continuumlabs.ai/models/foundation-models/llama-2-analysis.md): Meta introduced Llama 2 during June 2023
- [Analysis of Llama 3](https://training.continuumlabs.ai/models/foundation-models/analysis-of-llama-3.md)
- [Llama 3.1 series](https://training.continuumlabs.ai/models/foundation-models/llama-3.1-series.md)
- [Google Gemini 1.5](https://training.continuumlabs.ai/models/foundation-models/google-gemini-1.5.md)
- [Platypus: Quick, Cheap, and Powerful Refinement of LLMs](https://training.continuumlabs.ai/models/foundation-models/platypus-quick-cheap-and-powerful-refinement-of-llms.md)
- [Mixtral of Experts](https://training.continuumlabs.ai/models/foundation-models/mixtral-of-experts.md)
- [Mixture-of-Agents (MoA)](https://training.continuumlabs.ai/models/foundation-models/mixture-of-agents-moa.md)
- [Phi 1.5](https://training.continuumlabs.ai/models/foundation-models/phi-1.5.md): The Diminutive Giant: How the Phi Model is Revolutionising AI Accessibility
- [Refining the Art of AI Training: A Deep Dive into Phi 1.5's Innovative Approach](https://training.continuumlabs.ai/models/foundation-models/phi-1.5/refining-the-art-of-ai-training-a-deep-dive-into-phi-1.5s-innovative-approach.md): A long list of lessons, tips and tricks from the team that bought us Phi
- [Phi 2.0](https://training.continuumlabs.ai/models/foundation-models/phi-2.0.md): Microsoft's small but powerful transformer model
- [Phi-3 Technical Report](https://training.continuumlabs.ai/models/foundation-models/phi-3-technical-report.md): A Highly Capable Language Model Locally on Your Phone
- [The Fine Tuning Process](https://training.continuumlabs.ai/training/the-fine-tuning-process.md): Fine tuning deep learning models is completely different to fine tuning machine learning models
- [Why fine tune?](https://training.continuumlabs.ai/training/the-fine-tuning-process/why-fine-tune.md)
- [Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?](https://training.continuumlabs.ai/training/the-fine-tuning-process/why-fine-tune/does-fine-tuning-llms-on-new-knowledge-encourage-hallucinations.md)
- [Explanations in Fine Tuning](https://training.continuumlabs.ai/training/the-fine-tuning-process/why-fine-tune/explanations-in-fine-tuning.md)
- [Tokenization](https://training.continuumlabs.ai/training/the-fine-tuning-process/tokenization.md)
- [Tokenization Is More Than Compression](https://training.continuumlabs.ai/training/the-fine-tuning-process/tokenization/tokenization-is-more-than-compression.md): Craig W. Schmidt, Varshini Reddy, Haoran Zhang, Alec Alameddine, Omri Uzan, Yuval Pinter, Chris Tanner
- [Tokenization - SentencePiece](https://training.continuumlabs.ai/training/the-fine-tuning-process/tokenization/tokenization-sentencepiece.md): The Unsupervised Text Tokenizer for Neural Networks
- [Tokenization explore](https://training.continuumlabs.ai/training/the-fine-tuning-process/tokenization/tokenization-explore.md)
- [Tokenizer Choice For LLM Training: Negligible or Crucial?](https://training.continuumlabs.ai/training/the-fine-tuning-process/tokenization/tokenizer-choice-for-llm-training-negligible-or-crucial.md)
- [Getting the most out of your tokenizer for pre-training and domain adaptation](https://training.continuumlabs.ai/training/the-fine-tuning-process/tokenization/getting-the-most-out-of-your-tokenizer-for-pre-training-and-domain-adaptation.md)
- [TokenMonster](https://training.continuumlabs.ai/training/the-fine-tuning-process/tokenization/tokenmonster.md)
- [Parameter Efficient Fine Tuning](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning.md)
- [P-Tuning](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/p-tuning.md): The highly cited "GPT Understands Too" paper first submitted March 2021, introducing P-Tuning
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/p-tuning/the-power-of-scale-for-parameter-efficient-prompt-tuning.md)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/prefix-tuning-optimizing-continuous-prompts-for-generation.md)
- [Harnessing the Power of PEFT: A Smarter Approach to Fine-tuning Pre-trained Models](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/harnessing-the-power-of-peft-a-smarter-approach-to-fine-tuning-pre-trained-models.md): Parameter-Efficient Fine-Tuning (PEFT) is a technique used to fine tune neural language models
- [What is Low-Rank Adaptation (LoRA) -  explained by the inventor](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/what-is-low-rank-adaptation-lora-explained-by-the-inventor.md): Edward Hu
- [Low Rank Adaptation (Lora)](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/low-rank-adaptation-lora.md)
- [Practical Tips for Fine-tuning LMs Using LoRA (Low-Rank Adaptation)](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/practical-tips-for-fine-tuning-lms-using-lora-low-rank-adaptation.md)
- [QLORA: Efficient Finetuning of Quantized LLMs](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/qlora-efficient-finetuning-of-quantized-llms.md)
- [Bits and Bytes](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/bits-and-bytes.md): Tim Dettmers (PhD candidate, University of Washington) presents "8-bit Methods for Efficient Deep Learning" in this Cohere For AI Technical Talk.
- [The Magic behind Qlora](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/the-magic-behind-qlora.md)
- [Practical Guide to LoRA: Tips and Tricks for Effective Model Adaptation](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/practical-guide-to-lora-tips-and-tricks-for-effective-model-adaptation.md): A range of practical tips and questions around using Lora
- [The quantization constant](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/the-quantization-constant.md)
- [QLORA: Efficient Finetuning of Quantized Language Models](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/qlora-efficient-finetuning-of-quantized-language-models.md)
- [QLORA and Fine-Tuning of Quantized Language Models (LMs)](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/qlora-and-fine-tuning-of-quantized-language-models-lms.md)
- [ReLoRA: High-Rank Training Through Low-Rank Updates](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/relora-high-rank-training-through-low-rank-updates.md)
- [SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/slora-federated-parameter-efficient-fine-tuning-of-language-models.md): Leveraging Lora
- [GaLora: Memory-Efficient LLM Training by Gradient Low-Rank Projection](https://training.continuumlabs.ai/training/the-fine-tuning-process/parameter-efficient-fine-tuning/galora-memory-efficient-llm-training-by-gradient-low-rank-projection.md)
- [Hyperparameters](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters.md): Art and science
- [Batch Size](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/batch-size.md): Choosing the right batch size is critical
- [Padding Tokens](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/padding-tokens.md)
- [Mixed precision training](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/mixed-precision-training.md)
- [FP8 Formats for Deep Learning](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/fp8-formats-for-deep-learning.md)
- [Floating Point Numbers](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/floating-point-numbers.md)
- [Batch Size and Model loss](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/batch-size-and-model-loss.md)
- [Batch Normalisation](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/batch-normalisation.md)
- [Rethinking Learning Rate Tuning in the Era of Language Models](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/rethinking-learning-rate-tuning-in-the-era-of-language-models.md): One of the most important hyperparameters
- [Sample Packing](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/sample-packing.md)
- [Gradient accumulation](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/gradient-accumulation.md)
- [A process for choosing the learning rate](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/a-process-for-choosing-the-learning-rate.md)
- [Learning Rate Scheduler](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/learning-rate-scheduler.md): Key Considerations with Learning Rate Scheduling in Neural Network Training
- [Checkpoints](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/checkpoints.md)
- [A Survey on Efficient Training of Transformers](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/a-survey-on-efficient-training-of-transformers.md)
- [Sequence Length Warmup](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/sequence-length-warmup.md)
- [Understanding Training vs. Evaluation Data Splits](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/understanding-training-vs.-evaluation-data-splits.md)
- [Cross-entropy loss](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/cross-entropy-loss.md)
- [Weight Decay](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/weight-decay.md)
- [Optimiser](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/optimiser.md)
- [Caching](https://training.continuumlabs.ai/training/the-fine-tuning-process/hyperparameters/caching.md)
- [Training Processes](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes.md)
- [Extending the context window](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/extending-the-context-window.md)
- [PyTorch Fully Sharded Data Parallel (FSDP)](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/pytorch-fully-sharded-data-parallel-fsdp.md)
- [Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/train-short-test-long-attention-with-linear-biases-enables-input-length-extrapolation.md): Ofir Press, Noah A. Smith, Mike Lewis
- [YaRN: Efficient Context Window Extension of Large Language Models](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/yarn-efficient-context-window-extension-of-large-language-models.md): Nous Research, EleutherAI, University of Geneva
- [Sliding Window Attention](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/sliding-window-attention.md): Iz Beltagy, Matthew E. Peters, and Arman Cohan
- [LongRoPE](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/longrope.md)
- [Reinforcement Learning](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/reinforcement-learning.md)
- [An introduction to reinforcement learning](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/an-introduction-to-reinforcement-learning.md)
- [Reinforcement Learning from Human Feedback (RLHF)](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/reinforcement-learning-from-human-feedback-rlhf.md): Most often useful when creating domain specific models
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/direct-preference-optimization-your-language-model-is-secretly-a-reward-model.md)
- [Why is inference important?](https://training.continuumlabs.ai/inference/why-is-inference-important.md): Speed and cost counts
- [Grouped Query Attention](https://training.continuumlabs.ai/inference/why-is-inference-important/grouped-query-attention.md)
- [Key Value Cache](https://training.continuumlabs.ai/inference/why-is-inference-important/key-value-cache.md): Managing model memory usage
- [Flash Attention](https://training.continuumlabs.ai/inference/why-is-inference-important/flash-attention.md)
- [Flash Attention 2](https://training.continuumlabs.ai/inference/why-is-inference-important/flash-attention-2.md): The seminal July 2023 paper
- [StreamingLLM](https://training.continuumlabs.ai/inference/why-is-inference-important/streamingllm.md)
- [Paged Attention and vLLM](https://training.continuumlabs.ai/inference/why-is-inference-important/paged-attention-and-vllm.md)
- [TensorRT-LLM](https://training.continuumlabs.ai/inference/why-is-inference-important/tensorrt-llm.md)
- [Torchscript](https://training.continuumlabs.ai/inference/why-is-inference-important/torchscript.md)
- [NVIDIA L40S GPU](https://training.continuumlabs.ai/inference/why-is-inference-important/nvidia-l40s-gpu.md): Low cost inference
- [Triton Inference Server - Introduction](https://training.continuumlabs.ai/inference/why-is-inference-important/triton-inference-server-introduction.md)
- [Triton Inference Server](https://training.continuumlabs.ai/inference/why-is-inference-important/triton-inference-server.md)
- [FiDO: Fusion-in-Decoder optimised for stronger performance and faster inference](https://training.continuumlabs.ai/inference/why-is-inference-important/fido-fusion-in-decoder-optimised-for-stronger-performance-and-faster-inference.md): Google Research, December 2022
- [Is PUE a useful measure of data centre performance?](https://training.continuumlabs.ai/inference/why-is-inference-important/is-pue-a-useful-measure-of-data-centre-performance.md)
- [SLORA](https://training.continuumlabs.ai/inference/why-is-inference-important/slora.md)
- [Vector Databases](https://training.continuumlabs.ai/knowledge/vector-databases.md)
- [A Comprehensive Survey on Vector Databases](https://training.continuumlabs.ai/knowledge/vector-databases/a-comprehensive-survey-on-vector-databases.md)
- [Vector database management systems: Fundamental concepts, use-cases, and current challenges](https://training.continuumlabs.ai/knowledge/vector-databases/vector-database-management-systems-fundamental-concepts-use-cases-and-current-challenges.md)
- [Using the Output Embedding to Improve Language Models](https://training.continuumlabs.ai/knowledge/vector-databases/using-the-output-embedding-to-improve-language-models.md)
- [Decoding Sentence-BERT](https://training.continuumlabs.ai/knowledge/vector-databases/decoding-sentence-bert.md)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://training.continuumlabs.ai/knowledge/vector-databases/colbert-efficient-and-effective-passage-search-via-contextualized-late-interaction-over-bert.md): The widely cited paper by Omar Khattab and Matei Zaharia from Stanford University
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://training.continuumlabs.ai/knowledge/vector-databases/simcse-simple-contrastive-learning-of-sentence-embeddings.md)
- [Questions Are All You Need to Train a Dense Passage Retriever](https://training.continuumlabs.ai/knowledge/vector-databases/questions-are-all-you-need-to-train-a-dense-passage-retriever.md)
- [Improving Text Embeddings with Large Language Models](https://training.continuumlabs.ai/knowledge/vector-databases/improving-text-embeddings-with-large-language-models.md): Microsoft Corporation
- [Massive Text Embedding Benchmark](https://training.continuumlabs.ai/knowledge/vector-databases/massive-text-embedding-benchmark.md): The leaderboard for embedding models
- [RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking](https://training.continuumlabs.ai/knowledge/vector-databases/rocketqav2-a-joint-training-method-for-dense-passage-retrieval-and-passage-re-ranking.md)
- [LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders](https://training.continuumlabs.ai/knowledge/vector-databases/llm2vec-large-language-models-are-secretly-powerful-text-encoders.md)
- [Embedding and Fine-Tuning in Neural Language Models](https://training.continuumlabs.ai/knowledge/vector-databases/embedding-and-fine-tuning-in-neural-language-models.md): Mathematical representations of text
- [Embedding Model Construction](https://training.continuumlabs.ai/knowledge/vector-databases/embedding-model-construction.md)
- [Demystifying Embedding Spaces using Large Language Models](https://training.continuumlabs.ai/knowledge/vector-databases/demystifying-embedding-spaces-using-large-language-models.md): Guy Tennenholtz et al. from Google Research
- [Fine-Tuning Llama for Multi-Stage Text Retrieval](https://training.continuumlabs.ai/knowledge/vector-databases/fine-tuning-llama-for-multi-stage-text-retrieval.md): Microsoft Research
- [Large Language Model Based Text Augmentation Enhanced Personality Detection Model](https://training.continuumlabs.ai/knowledge/vector-databases/large-language-model-based-text-augmentation-enhanced-personality-detection-model.md)
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://training.continuumlabs.ai/knowledge/vector-databases/one-embedder-any-task-instruction-finetuned-text-embeddings.md)
- [Vector Databases are not the only solution](https://training.continuumlabs.ai/knowledge/vector-databases/vector-databases-are-not-the-only-solution.md): Yingjun Wu
- [Knowledge Graphs](https://training.continuumlabs.ai/knowledge/vector-databases/knowledge-graphs.md): Analysis of Knowledge Graphs and Influence of Generative AI and LLMs
- [Harnessing Knowledge Graphs to Elevate AI: A Technical Exploration](https://training.continuumlabs.ai/knowledge/vector-databases/knowledge-graphs/harnessing-knowledge-graphs-to-elevate-ai-a-technical-exploration.md): Finally a semantic data architecture
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://training.continuumlabs.ai/knowledge/vector-databases/knowledge-graphs/unifying-large-language-models-and-knowledge-graphs-a-roadmap.md)
- [Approximate Nearest Neighbor (ANN)](https://training.continuumlabs.ai/knowledge/vector-databases/approximate-nearest-neighbor-ann.md)
- [High Dimensional Data](https://training.continuumlabs.ai/knowledge/vector-databases/high-dimensional-data.md)
- [Principal Component Analysis (PCA)](https://training.continuumlabs.ai/knowledge/vector-databases/principal-component-analysis-pca.md)
- [Vector Similarity Search - HNSW](https://training.continuumlabs.ai/knowledge/vector-databases/vector-similarity-search-hnsw.md)
- [FAISS (Facebook AI Similarity Search)](https://training.continuumlabs.ai/knowledge/vector-databases/faiss-facebook-ai-similarity-search.md)
- [Unsupervised Dense Retrievers](https://training.continuumlabs.ai/knowledge/vector-databases/unsupervised-dense-retrievers.md)
- [Retrieval Augmented Generation](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation.md): A critical piece of the generative AI infrastructure
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/retrieval-augmented-generation-for-large-language-models-a-survey.md)
- [Fine-Tuning or Retrieval?](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/fine-tuning-or-retrieval.md): Microsoft, Israel
- [Revolutionising Information Retrieval: The Power of RAG in Language Models](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/revolutionising-information-retrieval-the-power-of-rag-in-language-models.md)
- [A Survey on Retrieval-Augmented Text Generation](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/a-survey-on-retrieval-augmented-text-generation.md): Huayang Li, Yixuan Su, Deng Cai, Yan Wang, Lemao Liu
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/realm-retrieval-augmented-language-model-pre-training.md): Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, Ming-Wei Chang
- [Retrieve Anything To Augment Large Language Models](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/retrieve-anything-to-augment-large-language-models.md)
- [Generate Rather Than Retrieve: Large Language Models Are Strong Context Generators](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/generate-rather-than-retrieve-large-language-models-are-strong-context-generators.md)
- [Active Retrieval Augmented Generation](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/active-retrieval-augmented-generation.md): FLARE!
- [DSPy: LM Assertions: Enhancing Language Model Pipelines with Computational Constraints](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/dspy-lm-assertions-enhancing-language-model-pipelines-with-computational-constraints.md)
- [DSPy: Compiling Declarative Language Model Calls](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/dspy-compiling-declarative-language-model-calls.md)
- [DSPy: In-Context Learning for Extreme Multi-Label Classification](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/dspy-in-context-learning-for-extreme-multi-label-classification.md)
- [Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/optimizing-instructions-and-demonstrations-for-multi-stage-language-model-programs.md)
- [HYDE: Revolutionising Search with Hypothetical Document Embeddings](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/hyde-revolutionising-search-with-hypothetical-document-embeddings.md)
- [Enhancing Recommender Systems with Large Language Model Reasoning Graphs](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/enhancing-recommender-systems-with-large-language-model-reasoning-graphs.md)
- [Retrieval Augmented Generation (RAG) versus fine tuning](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/retrieval-augmented-generation-rag-versus-fine-tuning.md)
- [RAFT: Adapting Language Model to Domain Specific RAG](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/raft-adapting-language-model-to-domain-specific-rag.md)
- [Summarisation Methods and RAG](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/summarisation-methods-and-rag.md)
- [Lessons Learned on LLM RAG Solutions](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/lessons-learned-on-llm-rag-solutions.md): Injecting data via embedding model into your vector database for future retrieval
- [Stanford: Retrieval Augmented Language Models](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/stanford-retrieval-augmented-language-models.md): Youtube Lecture - January 2024
- [Overview of RAG Approaches with Vector Databases](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/overview-of-rag-approaches-with-vector-databases.md): Some random tips on RAG and vector databases
- [Mastering Chunking in Retrieval-Augmented Generation (RAG) Systems](https://training.continuumlabs.ai/knowledge/retrieval-augmented-generation/mastering-chunking-in-retrieval-augmented-generation-rag-systems.md)
- [Semantic Routing](https://training.continuumlabs.ai/knowledge/semantic-routing.md)
- [Resource Description Framework (RDF)](https://training.continuumlabs.ai/knowledge/resource-description-framework-rdf.md)
- [What is agency?](https://training.continuumlabs.ai/agents/what-is-agency.md): Philosophical Origins of the Agent Concept
- [Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves](https://training.continuumlabs.ai/agents/what-is-agency/rephrase-and-respond-let-large-language-models-ask-better-questions-for-themselves.md): Yihe Deng, Weitong Zhang, Zixiang Chen, Quanquan Gu (University of California, Los Angeles)
- [Types of Agents](https://training.continuumlabs.ai/agents/what-is-agency/types-of-agents.md)
- [The risk of AI agency](https://training.continuumlabs.ai/agents/what-is-agency/the-risk-of-ai-agency.md): The concept of AI agency is powerful, but comes with an array of risks
- [Understanding Personality in Large Language Models: A New Frontier in AI Psychology](https://training.continuumlabs.ai/agents/what-is-agency/understanding-personality-in-large-language-models-a-new-frontier-in-ai-psychology.md)
- [AI Agents - Reasoning, Planning, and Tool Calling](https://training.continuumlabs.ai/agents/what-is-agency/ai-agents-reasoning-planning-and-tool-calling.md)
- [Personality and Brand](https://training.continuumlabs.ai/agents/what-is-agency/personality-and-brand.md): How can we create large language models that have personality?
- [Agent Interaction via APIs](https://training.continuumlabs.ai/agents/what-is-agency/agent-interaction-via-apis.md)
- [Bridging Minds and Machines: The Legacy of Newell, Shaw, and Simon](https://training.continuumlabs.ai/agents/what-is-agency/bridging-minds-and-machines-the-legacy-of-newell-shaw-and-simon.md)
- [A Survey on Language Model based Autonomous Agents](https://training.continuumlabs.ai/agents/what-is-agency/a-survey-on-language-model-based-autonomous-agents.md)
- [Large Language Models as Agents](https://training.continuumlabs.ai/agents/what-is-agency/large-language-models-as-agents.md): The Rise and Potential of Large Language Model Based Agents : A Survey
- [AI Reasoning: A Deep Dive into Chain-of-Thought Prompting](https://training.continuumlabs.ai/agents/what-is-agency/ai-reasoning-a-deep-dive-into-chain-of-thought-prompting.md)
- [Enhancing AI Reasoning with Self-Taught Reasoner (STaR)](https://training.continuumlabs.ai/agents/what-is-agency/enhancing-ai-reasoning-with-self-taught-reasoner-star.md)
- [Exploring the Frontier of AI: The "Tree of Thoughts" Framework](https://training.continuumlabs.ai/agents/what-is-agency/exploring-the-frontier-of-ai-the-tree-of-thoughts-framework.md): December 2023 paper
- [Toolformer: Revolutionising Language Models with API Integration - An Analysis](https://training.continuumlabs.ai/agents/what-is-agency/toolformer-revolutionising-language-models-with-api-integration-an-analysis.md)
- [TaskMatrix.AI: Bridging Foundational AI Models with Specialised Systems for Enhanced Task Completion](https://training.continuumlabs.ai/agents/what-is-agency/taskmatrix.ai-bridging-foundational-ai-models-with-specialised-systems-for-enhanced-task-completion.md)
- [Unleashing the Power of LLMs in API Integration: The Rise of Gorilla](https://training.continuumlabs.ai/agents/what-is-agency/unleashing-the-power-of-llms-in-api-integration-the-rise-of-gorilla.md)
- [Andrew Ng's presentation on AI agents](https://training.continuumlabs.ai/agents/what-is-agency/andrew-ngs-presentation-on-ai-agents.md)
- [Making AI accessible with Andrej Karpathy and Stephanie Zhan](https://training.continuumlabs.ai/agents/what-is-agency/making-ai-accessible-with-andrej-karpathy-and-stephanie-zhan.md)
- [Regulation and Ethics](https://training.continuumlabs.ai/regulation-and-ethics/regulation-and-ethics.md): December 2021 paper
- [Privacy](https://training.continuumlabs.ai/regulation-and-ethics/regulation-and-ethics/privacy.md)
- [Detecting AI Generated content](https://training.continuumlabs.ai/regulation-and-ethics/regulation-and-ethics/detecting-ai-generated-content.md): Ghostbuster - AI content detector
- [Navigating the IP Maze in AI: The Convergence of Blockchain, Web 3.0, and LLMs](https://training.continuumlabs.ai/regulation-and-ethics/regulation-and-ethics/navigating-the-ip-maze-in-ai-the-convergence-of-blockchain-web-3.0-and-llms.md): "Authorguard" - protecting content producers commercial interests
- [Adverse Reactions to generative AI](https://training.continuumlabs.ai/regulation-and-ethics/regulation-and-ethics/adverse-reactions-to-generative-ai.md)
- [Navigating the Ethical Minefield: The Challenge of Security in Large Language Models](https://training.continuumlabs.ai/regulation-and-ethics/regulation-and-ethics/navigating-the-ethical-minefield-the-challenge-of-security-in-large-language-models.md)
- [Navigating the Uncharted Waters: The Risks of Autonomous AI in Military Decision-Making](https://training.continuumlabs.ai/regulation-and-ethics/regulation-and-ethics/navigating-the-uncharted-waters-the-risks-of-autonomous-ai-in-military-decision-making.md): AI models seem to tend towards escalation...
- [Data Architecture](https://training.continuumlabs.ai/disruption/data-architecture.md)
- [What is a data pipeline?](https://training.continuumlabs.ai/disruption/data-architecture/what-is-a-data-pipeline.md)
- [What is Reverse ETL?](https://training.continuumlabs.ai/disruption/data-architecture/what-is-reverse-etl.md): Combining generative AI with Reverse ETL in Modern Businesses
- [Unstructured Data and Generatve AI](https://training.continuumlabs.ai/disruption/data-architecture/unstructured-data-and-generatve-ai.md)
- [Resource Description Framework (RDF)](https://training.continuumlabs.ai/disruption/data-architecture/resource-description-framework-rdf.md)
- [Integrating generative AI with the Semantic Web](https://training.continuumlabs.ai/disruption/data-architecture/integrating-generative-ai-with-the-semantic-web.md)
- [Search](https://training.continuumlabs.ai/disruption/search.md): Generative AI and its relationship with search
- [BM25 - Search Engine Ranking Function](https://training.continuumlabs.ai/disruption/search/bm25-search-engine-ranking-function.md)
- [BERT as a reranking engine](https://training.continuumlabs.ai/disruption/search/bert-as-a-reranking-engine.md): Retrieval and Reranking
- [BERT and Google](https://training.continuumlabs.ai/disruption/search/bert-and-google.md)
- [Generative Engine Optimisation (GEO)](https://training.continuumlabs.ai/disruption/search/generative-engine-optimisation-geo.md): Navigating the New Frontier: A Guide to Generative Engine Optimisation
- [Billion-scale similarity search with GPUs](https://training.continuumlabs.ai/disruption/search/billion-scale-similarity-search-with-gpus.md)
- [FOLLOWIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions](https://training.continuumlabs.ai/disruption/search/followir-evaluating-and-teaching-information-retrieval-models-to-follow-instructions.md)
- [Neural Collaborative Filtering](https://training.continuumlabs.ai/disruption/search/neural-collaborative-filtering.md): The highly popular 2017 paper that drove the advance of recommendation systems
- [Federated Neural Collaborative Filtering](https://training.continuumlabs.ai/disruption/search/federated-neural-collaborative-filtering.md): Collabative Filtering - Matching Consumers with Products and Services with Privacy
- [Latent Space versus Embedding Space](https://training.continuumlabs.ai/disruption/search/latent-space-versus-embedding-space.md)
- [Improving Text Embeddings with Large Language Models](https://training.continuumlabs.ai/disruption/search/improving-text-embeddings-with-large-language-models.md): Liang Wang and the Microsoft team
- [Recommendation Engines](https://training.continuumlabs.ai/disruption/recommendation-engines.md): There is major disruption coming to the recommendation engine industry
- [On Interpretation and Measurement of Soft Attributes for Recommendation](https://training.continuumlabs.ai/disruption/recommendation-engines/on-interpretation-and-measurement-of-soft-attributes-for-recommendation.md)
- [A Survey on Large Language Models for Recommendation](https://training.continuumlabs.ai/disruption/recommendation-engines/a-survey-on-large-language-models-for-recommendation.md)
- [Model driven recommendation systems](https://training.continuumlabs.ai/disruption/recommendation-engines/model-driven-recommendation-systems.md)
- [Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations](https://training.continuumlabs.ai/disruption/recommendation-engines/recommender-ai-agent-integrating-large-language-models-for-interactive-recommendations.md)
- [Foundation Models for Recommender Systems](https://training.continuumlabs.ai/disruption/recommendation-engines/foundation-models-for-recommender-systems.md)
- [Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review](https://training.continuumlabs.ai/disruption/recommendation-engines/exploring-the-impact-of-large-language-models-on-recommender-systems-an-extensive-review.md)
- [AI driven recommendations - harming autonomy?](https://training.continuumlabs.ai/disruption/recommendation-engines/ai-driven-recommendations-harming-autonomy.md): "Artificial intelligence vs. autonomous decision-making in streaming platforms: A mixed-method approach" by Ana Rita Gonçalves, Diego Costa Pinto, Saleh Shuqair, Marlon Dalmoro, and Anna S. Mattila
- [Logging](https://training.continuumlabs.ai/disruption/logging.md)
- [A Taxonomy of Anomalies in Log Data](https://training.continuumlabs.ai/disruption/logging/a-taxonomy-of-anomalies-in-log-data.md)
- [Deeplog](https://training.continuumlabs.ai/disruption/logging/deeplog.md)
- [LogBERT: Log Anomaly Detection via BERT](https://training.continuumlabs.ai/disruption/logging/logbert-log-anomaly-detection-via-bert.md)
- [Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection](https://training.continuumlabs.ai/disruption/logging/experience-report-deep-learning-based-system-log-analysis-for-anomaly-detection.md)
- [Log-based Anomaly Detection with Deep Learning: How Far Are We?](https://training.continuumlabs.ai/disruption/logging/log-based-anomaly-detection-with-deep-learning-how-far-are-we.md)
- [Deep Learning for Anomaly Detection in Log Data: A Survey](https://training.continuumlabs.ai/disruption/logging/deep-learning-for-anomaly-detection-in-log-data-a-survey.md)
- [LogGPT](https://training.continuumlabs.ai/disruption/logging/loggpt.md)
- [Adaptive Semantic Gate Networks (ASGNet) for log-based anomaly diagnosis](https://training.continuumlabs.ai/disruption/logging/adaptive-semantic-gate-networks-asgnet-for-log-based-anomaly-diagnosis.md)
- [The modern data centre](https://training.continuumlabs.ai/infrastructure/the-modern-data-centre.md)
- [Enhancing Data Centre Efficiency: Strategies to Improve PUE](https://training.continuumlabs.ai/infrastructure/the-modern-data-centre/enhancing-data-centre-efficiency-strategies-to-improve-pue.md)
- [TCO of NVIDIA GPUs and falling barriers to entry](https://training.continuumlabs.ai/infrastructure/the-modern-data-centre/tco-of-nvidia-gpus-and-falling-barriers-to-entry.md)
- [Maximising GPU Utilisation with Kubernetes and NVIDIA GPU Operator](https://training.continuumlabs.ai/infrastructure/the-modern-data-centre/maximising-gpu-utilisation-with-kubernetes-and-nvidia-gpu-operator.md)
- [Data Centres](https://training.continuumlabs.ai/infrastructure/the-modern-data-centre/data-centres.md)
- [Liquid Cooling](https://training.continuumlabs.ai/infrastructure/the-modern-data-centre/liquid-cooling.md)
- [Servers and Chips](https://training.continuumlabs.ai/infrastructure/servers-and-chips.md)
- [The NVIDIA H100 GPU](https://training.continuumlabs.ai/infrastructure/servers-and-chips/the-nvidia-h100-gpu.md)
- [NVIDIA H100 NVL](https://training.continuumlabs.ai/infrastructure/servers-and-chips/nvidia-h100-nvl.md): For large language model inference workloads
- [Lambda Hyperplane 8-H100](https://training.continuumlabs.ai/infrastructure/servers-and-chips/lambda-hyperplane-8-h100.md)
- [NVIDIA DGX Servers](https://training.continuumlabs.ai/infrastructure/servers-and-chips/nvidia-dgx-servers.md)
- [NVIDIA DGX-2](https://training.continuumlabs.ai/infrastructure/servers-and-chips/nvidia-dgx-2.md)
- [NVIDIA DGX H-100 System](https://training.continuumlabs.ai/infrastructure/servers-and-chips/nvidia-dgx-h-100-system.md): An absolute beast
- [NVLink Switch](https://training.continuumlabs.ai/infrastructure/servers-and-chips/nvlink-switch.md): Rapid Communication between GPUs
- [Tensor Cores](https://training.continuumlabs.ai/infrastructure/servers-and-chips/tensor-cores.md)
- [NVIDIA Grace Hopper Superchip](https://training.continuumlabs.ai/infrastructure/servers-and-chips/nvidia-grace-hopper-superchip.md)
- [NVIDIA Grace CPU Superchip](https://training.continuumlabs.ai/infrastructure/servers-and-chips/nvidia-grace-cpu-superchip.md)
- [NVIDIA GB200 NVL72](https://training.continuumlabs.ai/infrastructure/servers-and-chips/nvidia-gb200-nvl72.md)
- [Hopper versus Blackwell](https://training.continuumlabs.ai/infrastructure/servers-and-chips/hopper-versus-blackwell.md): A comparison between the two latest GPU servers
- [HGX: High-Performance GPU Platforms](https://training.continuumlabs.ai/infrastructure/servers-and-chips/hgx-high-performance-gpu-platforms.md)
- [ARM Chips](https://training.continuumlabs.ai/infrastructure/servers-and-chips/arm-chips.md)
- [ARM versus x86](https://training.continuumlabs.ai/infrastructure/servers-and-chips/arm-versus-x86.md)
- [RISC versus CISC](https://training.continuumlabs.ai/infrastructure/servers-and-chips/risc-versus-cisc.md)
- [Introduction to RISC-V](https://training.continuumlabs.ai/infrastructure/servers-and-chips/introduction-to-risc-v.md)
- [Networking and Connectivity](https://training.continuumlabs.ai/infrastructure/networking-and-connectivity.md)
- [Infiniband versus Ethernet](https://training.continuumlabs.ai/infrastructure/networking-and-connectivity/infiniband-versus-ethernet.md): Networking Technologies
- [NVIDIA Quantum InfiniBand](https://training.continuumlabs.ai/infrastructure/networking-and-connectivity/nvidia-quantum-infiniband.md): Networking Solution
- [PCIe (Peripheral Component Interconnect Express)](https://training.continuumlabs.ai/infrastructure/networking-and-connectivity/pcie-peripheral-component-interconnect-express.md)
- [NVIDIA ConnectX InfiniBand adapters](https://training.continuumlabs.ai/infrastructure/networking-and-connectivity/nvidia-connectx-infiniband-adapters.md)
- [NVMe (Non-Volatile Memory Express)](https://training.continuumlabs.ai/infrastructure/networking-and-connectivity/nvme-non-volatile-memory-express.md)
- [NVMe over Fabrics (NVMe-oF)](https://training.continuumlabs.ai/infrastructure/networking-and-connectivity/nvme-over-fabrics-nvme-of.md): A protocol that enables high-performance, low-latency access to shared storage resources over various network fabrics
- [NVIDIA Spectrum-X](https://training.continuumlabs.ai/infrastructure/networking-and-connectivity/nvidia-spectrum-x.md)
- [NVIDIA GPUDirect](https://training.continuumlabs.ai/infrastructure/networking-and-connectivity/nvidia-gpudirect.md)
- [Evaluating Modern GPU Interconnect](https://training.continuumlabs.ai/infrastructure/networking-and-connectivity/evaluating-modern-gpu-interconnect.md): Ang Li et al
- [Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)](https://training.continuumlabs.ai/infrastructure/networking-and-connectivity/scalable-hierarchical-aggregation-and-reduction-protocol-sharp.md)
- [Next-generation networking in AI environments](https://training.continuumlabs.ai/infrastructure/networking-and-connectivity/next-generation-networking-in-ai-environments.md)
- [NVIDIA Collective Communications Library (NCCL)](https://training.continuumlabs.ai/infrastructure/networking-and-connectivity/nvidia-collective-communications-library-nccl.md)
- [Data and Memory](https://training.continuumlabs.ai/infrastructure/data-and-memory.md)
- [NVIDIA BlueField Data Processing Units (DPUs)](https://training.continuumlabs.ai/infrastructure/data-and-memory/nvidia-bluefield-data-processing-units-dpus.md)
- [Remote Direct Memory Access (RDMA)](https://training.continuumlabs.ai/infrastructure/data-and-memory/remote-direct-memory-access-rdma.md)
- [High Bandwidth Memory (HBM3)](https://training.continuumlabs.ai/infrastructure/data-and-memory/high-bandwidth-memory-hbm3.md): SK Hynix Inc
- [Flash Memory](https://training.continuumlabs.ai/infrastructure/data-and-memory/flash-memory.md)
- [Model Requirements](https://training.continuumlabs.ai/infrastructure/data-and-memory/model-requirements.md)
- [Calculating GPU memory for serving LLMs](https://training.continuumlabs.ai/infrastructure/data-and-memory/calculating-gpu-memory-for-serving-llms.md)
- [Transformer training costs](https://training.continuumlabs.ai/infrastructure/data-and-memory/transformer-training-costs.md)
- [GPU Performance Optimisation](https://training.continuumlabs.ai/infrastructure/data-and-memory/gpu-performance-optimisation.md)
- [Libraries and Complements](https://training.continuumlabs.ai/infrastructure/libraries-and-complements.md)
- [NVIDIA Base Command](https://training.continuumlabs.ai/infrastructure/libraries-and-complements/nvidia-base-command.md)
- [NVIDIA AI Enterprise](https://training.continuumlabs.ai/infrastructure/libraries-and-complements/nvidia-ai-enterprise.md)
- [CUDA - NVIDIA GTC 2024 presentation](https://training.continuumlabs.ai/infrastructure/libraries-and-complements/cuda-nvidia-gtc-2024-presentation.md): Steven Jones' presentation on CUDA at NVIDIA GTC 2024
- [RAPIDs](https://training.continuumlabs.ai/infrastructure/libraries-and-complements/rapids.md)
- [RAFT](https://training.continuumlabs.ai/infrastructure/libraries-and-complements/raft.md)
- [Vast Data Platform](https://training.continuumlabs.ai/infrastructure/vast-data-platform.md)
- [Vast Datastore](https://training.continuumlabs.ai/infrastructure/vast-data-platform/vast-datastore.md)
- [Vast Database](https://training.continuumlabs.ai/infrastructure/vast-data-platform/vast-database.md)
- [Vast Data Engine](https://training.continuumlabs.ai/infrastructure/vast-data-platform/vast-data-engine.md)
- [DASE (Disaggregated and Shared Everything)](https://training.continuumlabs.ai/infrastructure/vast-data-platform/dase-disaggregated-and-shared-everything.md)
- [Dremio and VAST Data](https://training.continuumlabs.ai/infrastructure/vast-data-platform/dremio-and-vast-data.md)
- [Storage](https://training.continuumlabs.ai/infrastructure/storage.md)
- [WEKA: A High-Performance Storage Solution for AI Workloads](https://training.continuumlabs.ai/infrastructure/storage/weka-a-high-performance-storage-solution-for-ai-workloads.md)
- [Introduction to NVIDIA GPUDirect Storage (GDS)](https://training.continuumlabs.ai/infrastructure/storage/introduction-to-nvidia-gpudirect-storage-gds.md)
- [GDS cuFile API](https://training.continuumlabs.ai/infrastructure/storage/introduction-to-nvidia-gpudirect-storage-gds/gds-cufile-api.md)
- [NVIDIA Magnum IO GPUDirect Storage (GDS)](https://training.continuumlabs.ai/infrastructure/storage/nvidia-magnum-io-gpudirect-storage-gds.md)
- [Vectors in Memory](https://training.continuumlabs.ai/infrastructure/storage/vectors-in-memory.md)


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