Pushing the Boundaries of LLMs: Sparse & Flash Attention, Quantisation, Pruning, Distillation, LORA
Channel: DataTrek
Date: 2023-07-10

#LLMs #FlashAttention #SparseAttention #MultiQueryAttention #ConditionalComputation #Pruning #Distillation #Quantization #LORAFineTuning #RetrievalAugmentedLLMs #ResponsibleAI #AUTO-GPT


Conversational AI : Understanding the Technology behind Chat-GPT | GPT| RLHF | Few Shot Inferences https://youtu.be/JKoJ5YIr2O4
The Good, the Bad, & the Ugly : Exploring the Dual Nature of Conversational LLMs | Open-Source LLMs https://youtu.be/MHfzoHC4kek

In this video, I explore some of the key ingredients of large language models (LLMs) that can process over 100k+ context windows, such as:

Flash Attention, an IO-aware exact attention algorithm that uses tiling to reduce the number of memory reads/writes between GPU high bandwidth memory (HBM) and GPU on-chip SRAM. Flash Attention trains Transformers faster than existing baselines and enables longer context in Transformers.

Sparse Attention, an approximate attention method that only computes attention for a subset of tokens, reducing the time and memory complexity from quadratic to sub-quadratic. Sparse Attention can improve the performance and efficiency of LLMs on long-range tasks.

Multi-query Attention, an attention mechanism that allows each token to attend to multiple queries but keep key & vectors matrix same across heads. This reduces the memory footprints and enhances the training/inference speed.

Conditional Computation, a technique that dynamically activates or deactivates parts of the model based on the input, saving computation and memory resources. Conditional Computation can adapt LLMs to different domains and tasks with varying complexity and difficulty.

I also discuss some of the methods that can improve the efficiency and performance of LLMs, such as:

Pruning, a technique that removes redundant or unimportant parameters or neurons from the model, reducing its size and complexity without sacrificing accuracy. Pruning can make LLMs more compact and faster to deploy.

Distillation, a technique that transfers the knowledge from a large teacher model to a smaller student model, preserving its performance with fewer parameters. Distillation can make LLMs more accessible and scalable for various applications.

Quantization, a technique that reduces the precision of the model parameters or activations, lowering its memory footprint and computational cost. Quantization can make LLMs more hardware-friendly and energy-efficient.

LORA-based fine-tuning, a technique that adapts LLMs to specific domains and tasks with minimal data and computational resources using matrix decomposition reducing the number of learnable parameters. LORA-based fine-tuning uses low-rank adaptation layers on top of frozen LLMs, achieving state-of-the-art results on several NLP benchmarks.

Furthermore, I dive into the topic of retrieval-augmented LLMs, which can leverage external knowledge sources to enhance the diversity and relevance of the generated text. I show how these models can combine neural and symbolic reasoning to produce more informative and coherent outputs.

Finally, I touch upon the responsible AI research that aims to address the ethical and social issues associated with LLM outputs, such as bias, toxicity, misinformation, and privacy. I highlight some of the challenges and opportunities in this field, and suggest some best practices for developing and deploying LLMs in a responsible manner.

And don't miss the fascinating concept of AUTO-GPT, which is a self-learning framework that can automatically discover and optimize the architecture and hyperparameters of LLMs based on the data and task at hand. This could potentially revolutionize the field of natural language processing and open up new possibilities for LLM applications.

I hope you enjoy this video and learn something new from it. If you do, please like, share, comment, and subscribe to my channel for more videos on LLMs and related topics. Thank you for watching!

#datascience #machinelearning #statistics #deeplearning #programming #python #datatrek #youtube #interview #interviewpreparation #interviewquestions #datascientist #dataanalytics #machinelearningengineer #datasciencejobs #datasciencetraining #datasciencecourse #datascienceenthusiast #career #careeropportunities #careergrowth #careerdevelopment #datascienceenthusiast #interviewing #ml #ai

About DataTrek Series
https://youtu.be/YvUMvFVwC_E

Business Enquiries: collabwithdatatrek@gmail.com
Find me on Instagram: https://www.instagram.com/simplyspartanx/




Music: https://www.bensound.com/royalty-free-music