你知道开发一个大型语言模型需要涉及哪些知识吗？

Do you know what goes into developing an #LLM?

你知道开发一个大型语言模型需要涉及什么吗？

LLMs are the backbone of our GenAI applications and it is very important to understand what goes into creating these LLMs.

大型语言模型是生成式人工智能应用的支柱，理解创建这些大型语言模型需要什么是非常重要的。

Just to give you an idea, here is a very basic setup and it involves 3 stages.Here are the different stages of building an LLM.

为了让你有个概念，下面有一个非常基本的介绍，以下是构建一个大型语言模型的三个不同阶段。

Stage 1: Building（构建）

Stage 2: Pre-training（预训练）

Stage 3: Finetuning（微调）

⮕ Building Stage（构建阶段）：

⦿ Data Preparation: Involves collecting and preparing datasets.

⦿ 数据准备：包括收集和准备数据集。

⦿ Model Architecture: Implementing the attention mechanism and overall architecture

⦿ 模型架构：实施注意力机制和整体架构。

⮕ Pre-Training Stage:

⦿ Training Loop: Using a large dataset to train the model to predict the next word in a sentence.

⦿ 训练循环：使用一个大型数据集来训练模型以预测句子中的下一个单词。

⦿ Foundation Models: The pre-training stage creates a base model for further fine-tuning.

⦿ 基础模型：通过预训练阶段就创建了一个用于进一步微调的基础模型。

⮕ Fine-Tuning Stage（ 微调阶段）:

⦿ Classification Tasks: Adapting the model for specific tasks like text categorization and spam detection.

⦿ 分类任务：使模型适应特定任务，如文本分类和垃圾邮件检测。

⦿ Instruction Fine-Tuning: Creating personal assistants or chatbots using instruction datasets.

⦿ 指令微调：使用指令数据集创建个人助手或聊天机器人。

Modern LLMs are trained on vast datasets, with a trend toward increasing the size for better performance.

现代大型语言模型是在庞大的数据集上进行训练的，有一种趋势是为了获得更好的性能而增加模型规模（大小）。

The above explained process is just the tip of the iceberg but its a very complex process that goes into building an LLM. It takes hours to explain this but just know that developing an LLM involves gathering massive text datasets, using self-supervised techniques to pretrain on that data, scaling the model to have billions of parameters, leveraging immense computational resources for training, evaluating capabilities through benchmarks, fine-tuning for specific tasks, and implementing safety constraints.

上面解释的过程只是冰山一角，构建一个大型语言模型是一个非常复杂的过程。这需要几个小时来解释，但要知道开发一个大型语言模型涉及收集大量文本数据集，使用自监督技术在该数据上进行预训练，将模型扩展到拥有数十亿，数百亿个参数，利用巨大的计算资源进行训练，通过基准测试评估能力，针对特定任务进行微调，并实施安全约束。