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This tutorial guides readers on how to fine-tune the Mistral 7B large language model using QLoRA with the Axolotl library, focusing on managing limited GPU resources for efficient training. It covers environment setup, dataset creation, configuration of QLoRA hyperparameters, the fine-tuning process, and testing the fine-tuned model.
This paper presents a method to accelerate the grokking phenomenon, where a model's generalization improves with more training iterations after an initial overfitting stage. The authors propose a simple algorithmic modification to existing optimizers that filters out the fast-varying components of the gradients and amplifies the slow-varying components, thereby accelerating the grokking effect.
This article discusses the process of training a large language model (LLM) using reinforcement learning from human feedback (RLHF) and a new alternative method called Direct Preference Optimization (DPO). The article explains how these methods help align the LLM with human expectations and make it more efficient.
Delving into transformer networks
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