Duration: (3:39) ?Subscribe5835 2025-02-09T23:01:24+00:00
How ChatGPT Cheaps Out Over Time
(9:28)
What is LLM Distillation ?
(6:17)
Amazon Bedrock Model Distillation Demo | Amazon Web Services
(4:11)
DeepSeek facts vs hype, model distillation, and open source competition
(39:17)
DeepSeek and distillation: Why the AI race will never be the same
(3:45)
Model Distillation: Same LLM Power but 3240x Smaller
(25:21)
OpenAI Believes DeepSeek ‘Distilled’ Its Data For Training—Here's What To Know About The Technique
(1:59)
Scholz gegen Merz | Das TV-Duell und die Analyse bei ZDFheute live
(2:59:54)
Richard Wolff: The FALL of the US Empire–US Denial, Europe Burns, BRICS \u0026 China Rise
(18:)
Holy SH*T! Putin and Trump are about to change EVERYTHING in Ukraine - Zelensky in panic mode!
(15:5)
L’Erreur Fatale à Éviter Quand Tu Crées du Contenu
(27:51)
Deepseek R1 \u0026 DeepSeek R1-Distill-Qwen-32B: Reasoning LM explained
(16:28)
DeepSeek R1 Hardware Requirements Explained
(5:6)
EfficientML.ai Lecture 9 - Knowledge Distillation (MIT 6.5940, Fall 2023)
(1:11)
Many are taking to the streets in protest against the rise of the far-right in Germany | DW News
(1:58)
[TA 補充課] Network Compression (1/2): Knowledge Distillation (由助教劉俊緯同學講授)
(1:7:53)
Quantization vs Pruning vs Distillation: Optimizing NNs for Inference
(19:46)
Deep Dive: Model Distillation with DistillKit
(45:19)
Better not Bigger: Distilling LLMs into Specialized Models
(16:49)
Deepseek R1 Explained by a Retired Microsoft Engineer
(10:7)
Model Distillation For ChatGPT: OpenAI Tutorial For Cost-Efficient AI
(5:57)
DeepSeek R1 Explained to your grandma
(8:33)
OpenAI DevDay 2024 | Tuning powerful small models with distillation
(30:50)
The Unreasonable Effectiveness of Reasoning Distillation: using DeepSeek R1 to beat OpenAI o1
(23:35)
A Slightly Technical Breakdown of DeepSeek-R1
(11:38)
Knowledge Distillation: A Good Teacher is Patient and Consistent
(12:35)
Big Tech in panic mode... Did DeepSeek R1 just pop the AI bubble?
(3:37)
MedAI #88: Distilling Step-by-Step! Outperforming LLMs with Smaller Model Sizes | Cheng-Yu Hsieh
(57:22)
Knowledge Distillation in Deep Learning - Basics
(9:51)