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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?；镜慕榻B，以下是構建一個大型語言模型的三個不同階段。
Stage 1: Building（構建）
Stage 2: Pre-training（預訓練）
Stage 3: Finetuning（微調）
? Building Stage（構建階段）：
? Data Preparation: Involves collecting and preparing datasets.
? 數據準備：包括收集和準備數據集。
? Model Architecture: Implementing the attention mechanism and overall architecture
? 模型架構：實施注意力機制和整體架構。
? Pre-Training Stage:
? Training Loop: Using a large dataset to train the model to predict the next word in a sentence.
? 訓練循環(huán)：使用一個大型數據集來訓練模型以預測句子中的下一個單詞。
? Foundation Models: The pre-training stage creates a base model for further fine-tuning.
? 基礎模型：通過預訓練階段就創(chuàng)建了一個用于進一步微調的基礎模型。
? Fine-Tuning Stage（?微調階段）:
? Classification Tasks: Adapting the model for specific tasks like text categorization and spam detection.
? 分類任務：使模型適應特定任務，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：使用指令數據集創(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.
現代大型語言模型是在龐大的數據集上進行訓練的，有一種趨勢是為了獲得更好的性能而增加模型規(guī)模（大?。?/em>
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，構建一個大型語言模型是一個非常復雜的過程。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據集，使用自監(jiān)督技術在該數據上進行預訓練，將模型擴展到擁有數十億，數百億個參數，利用巨大的計算資源進行訓練，通過基準測試評估能力，針對特定任務進行微調，并實施安全約束。
下載ECAD模型
