你知道開(kāi)發(fā)一個(gè)大型語(yǔ)言模型需要涉及哪些知識(shí)嗎？

Do you know what goes into developing an #LLM?

你知道開(kāi)發(fā)一個(gè)大型語(yǔ)言模型需要涉及什么嗎？

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

大型語(yǔ)言模型是生成式人工智能應(yīng)用的支柱，理解創(chuàng)建這些大型語(yǔ)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.

為了讓你有個(gè)概念，下面有一個(gè)非?；镜慕榻B，以下是構(gòu)建一個(gè)大型語(yǔ)言模型的三個(gè)不同階段。

Stage 1: Building（構(gòu)建）

Stage 2: Pre-training（預(yù)訓(xùn)練）

Stage 3: Finetuning（微調(diào)）

? Building Stage（構(gòu)建階段）：

? Data Preparation: Involves collecting and preparing datasets.

? 數(shù)據(jù)準(zhǔn)備：包括收集和準(zhǔn)備數(shù)據(jù)集。

? Model Architecture: Implementing the attention mechanism and overall architecture

? 模型架構(gòu)：實(shí)施注意力機(jī)制和整體架構(gòu)。

? Pre-Training Stage:

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

? 訓(xùn)練循環(huán)：使用一個(gè)大型數(shù)據(jù)集來(lái)訓(xùn)練模型以預(yù)測(cè)句子中的下一個(gè)單詞。

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

? 基礎(chǔ)模型：通過(guò)預(yù)訓(xùn)練階段就創(chuàng)建了一個(gè)用于進(jìn)一步微調(diào)的基礎(chǔ)模型。

? Fine-Tuning Stage（?微調(diào)階段）:

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

? 分類(lèi)任務(wù)：使模型適應(yīng)特定任務(wù)，如文本分類(lèi)和垃圾郵件檢測(cè)。

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

? 指令微調(diào)：使用指令數(shù)據(jù)集創(chuàng)建個(gè)人助手或聊天機(jī)器人。

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

現(xiàn)代大型語(yǔ)言模型是在龐大的數(shù)據(jù)集上進(jìn)行訓(xùn)練的，有一種趨勢(shì)是為了獲得更好的性能而增加模型規(guī)模（大?。?。

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.

上面解釋的過(guò)程只是冰山一角，構(gòu)建一個(gè)大型語(yǔ)言模型是一個(gè)非常復(fù)雜的過(guò)程。這需要幾個(gè)小時(shí)來(lái)解釋?zhuān)篱_(kāi)發(fā)一個(gè)大型語(yǔ)言模型涉及收集大量文本數(shù)據(jù)集，使用自監(jiān)督技術(shù)在該數(shù)據(jù)上進(jìn)行預(yù)訓(xùn)練，將模型擴(kuò)展到擁有數(shù)十億，數(shù)百億個(gè)參數(shù)，利用巨大的計(jì)算資源進(jìn)行訓(xùn)練，通過(guò)基準(zhǔn)測(cè)試評(píng)估能力，針對(duì)特定任務(wù)進(jìn)行微調(diào)，并實(shí)施安全約束。