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Deep Dive into GEMM-2B Fine-Tuning and Quantization Techniques

Deep Dive into GEMM-2B Fine-Tuning and Quantization Techniques **Description**: Join us as we delve into the fascinating world of GEMM-2B fine-tuning. In this session, we explore the inner workings of tokenizers and their role in Natural Language Processing. We also discuss the intriguing concepts of Post-Training Quantization (PTQ) and Aware Training Quantization (ATQ), shedding light on their importance in model optimization. The session further covers the differences and applications of Asymmetric and Symmetric Quantization. Finally, we walk you through the configuration of LoRa and demonstrate the code implementation of GEMM-2B. Don't miss out on this comprehensive guide to understanding and implementing these advanced techniques! ✌ Follow me On Github GitHub code: https://github.com/sajjadrahman56 🐤Twitter:   / sajjadrahman56   #GEMMA-2B,#GEMMA-2B-IT, #FineTuning, #Tokenizer, #Quantization, #PTQ, #ATQ, #AsymmetricQuantization, #SymmetricQuantization, #LoRa, #CodingTutorial, #MachineLearning #deeplearning , #ModelOptimization, #nlpslearns #cprogramming #cosolneproject #medicalsystem #programming c #programing #cse #cseengineering