你知道開發(fā)一個大型語言模型需要涉及哪些知識嗎？

Do you know what goes into developing an #LLM?

你知道開發(fā)一個大型語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.

為了讓你有個概念，下面有一個非常基本的介紹，以下是構(gòu)建一個大型語言模型的三個不同階段。

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)準備：包括收集和準備數(shù)據(jù)集。

? Model Architecture: Implementing the attention mechanism and overall architecture

? 模型架構(gòu)：實施注意力機制和整體架構(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)：使用一個大型數(shù)據(jù)集來訓(xùn)練模型以預(yù)測句子中的下一個單詞。

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

? 基礎(chǔ)模型：通過預(yù)訓(xùn)練階段就創(chuàng)建了一個用于進一步微調(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.

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

? 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)建個人助手或聊天機器人。

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

現(xiàn)代大型語言模型是在龐大的數(shù)據(jù)集上進行訓(xùn)練的，有一種趨勢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.

上面解釋的過程只是冰山一角，構(gòu)建一個大型語言模型是一個非常復(fù)雜的過程。這需要幾個小時來解釋，但要知道開發(fā)一個大型語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)上進行預(yù)訓(xùn)練，將模型擴展到擁有數(shù)十億，數(shù)百億個參數(shù)，利用巨大的計算資源進行訓(xùn)練，通過基準測試評估能力，針對特定任務(wù)進行微調(diào)，并實施安全約束。