Democratizing Foundation Models via k-bit Quantization - Tim Dettmers | Stanford MLSys #82 Published 2023-10-23 Download video MP4 360p Recommendations 55:59 Training LLMs at Scale - Deepak Narayanan | Stanford MLSys #83 56:32 Monarch Mixer: Making Foundation Models More Efficient - Dan Fu | Stanford MLSys #86 30:48 QLoRA: Efficient Finetuning of Quantized LLMs | Tim Dettmers 57:05 Text2SQL: The Dream versus Reality - Laurel Orr | Stanford MLSys #89 58:13 How Fine-tuning Open Source LLMs Solves GenAI Productionization - Piero Molino | Stanford MLSys #94 1:19:06 Hardware-aware Algorithms for Sequence Modeling - Tri Dao | Stanford MLSys #87 1:16:48 Notes on AI Hardware - Benjamin Spector | Stanford MLSys #88 57:58 QLoRA: Efficient Finetuning of Quantized Large Language Models (Tim Dettmers) 1:37:37 The Turing Lectures: The future of generative AI 17:07 LoRA explained (and a bit about precision and quantization) 1:01:53 Tim Dettmers | QLoRA: Efficient Finetuning of Quantized Large Language Models 58:26 A Guide to Parameter-Efficient Fine-Tuning - Vlad Lialin | Munich NLP Hands-on 021 27:26 22. Квантизация нейронных сетей. Иван Печенко 58:41 8-bit Methods for Efficient Deep Learning with Tim Dettmers Similar videos 06:46 Tim Dettmers—k-bit Inference Scaling Laws 12:16 8-bit Optimizers via Block-wise Quantization 1:06:53 AI on your phone? Tim Dettmers on quantization of neural networks — #41 26:49 MLT __init__ Session #17: LLM int8 3:06:41 QLoRA: Quantization for Fine Tuning 1:11:43 Lecture 05 - Quantization (Part I) | MIT 6.S965 58:07 ML for ML Compilers - Mangpo Phothilimthana | Stanford MLSys #80 58:29 A Taxonomy of ML for Systems Problems - Martin Maas | Stanford MLSys #81 More results