If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):4 stages of training LLMs from scratch, clearly explained (with visuals): ...

LLM Training Stages - LLM 从零开始训练包含四个阶段 [1] - 第一步是使用随机初始化的模型 [2] - 之后在大规模语料库上进行预训练 [2] - 使用指令微调使其能够遵循命令 [2] - 使用偏好和推理微调来优化响应 [2]

LLM Development Stages - The document outlines four stages for building Large Language Models (LLMs) from scratch for real-world applications [1] - These stages include pre-training, instruction fine-tuning, preference fine-tuning, and reasoning fine-tuning [1] Techniques Overview - The document indicates that these techniques are visually summarized [1]

LLM Training Stages - The document outlines 4 stages of training LLMs from scratch [1] Visual Aids - The explanation includes visuals for clarity [1]

Model Training Optimization - The industry has been training neural networks for 9 years [1] - The industry actively uses 16 ways to optimize model training [1]

Expertise & Focus - The author has 9 years of experience training neural networks [1] - The content focuses on optimizing model training in the fields of Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAGs) [1] Content Type - The author shares tutorials and insights daily on DS, ML, LLMs, and RAGs [1] - The content includes 16 ways to actively optimize model training [1]

Neural Network Optimization Techniques - The document highlights 16 techniques actively used to optimize neural network training [1] - The author encourages readers to share additional techniques in the replies [1]

Model Training Optimization - The industry has been training neural networks for 9 years [1] - The industry actively uses 16 ways to optimize model training [1]

That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):Andrew Ng's team once made a big mistake in a research paper.And it happened due to randomly splitting the data.Here's what happened: ...

This led to data leakage, and validation scores looked much better than they should have.A few days later, the team updated the paper after using the group shuffle split strategy to ensure the same patients did not end up in both the training and validation sets.Check this 👇 https://t.co/1UM6PRYdQz ...