Large Language Model(LLM) 🤔🤔
LLMs, or Large Language Models, are a specialized type of language model distinguished by their enormous number of parameters, typically ranging from 10 to 100 billions.
➡️Understanding API and Hugging Face:
APIs (Application Programming Interfaces) provide an interface for software applications to communicate and interact with each other. Hugging Face is a platform that offers a wide range of open-source NLP resources, tools, and models.Utilizing Hugging Face Transformers, developers can leverage pre-trained models for tasks like summarization, sentiment analysis, and conversational AI.
➡️ Deep Dive into Building an LLM Model from Scratch:
1️⃣ Data Curation
▫️Source and Filter Data:
Gather data from the internet, including web pages, articles, and conversations. Filter out low-quality, irrelevant, or toxic content using classifiers or heuristics.
▫️De-duplication:
Eliminate identical or very similar text instances to ensure diversity and prevent bias in the dataset.
▫️Privacy Redaction:
Remove personally identifiable information to safeguard sensitive data.
▫️Tokenization:
Transform text into numerical form using methods like bytepair encoding (BPE) for efficient processing.
2️⃣ Model Architecture
▫️Transformer Architecture:
Utilize transformer models employing attention mechanisms to understand data sequences. Options include encoder-only (like BERT), decoder-only (like GPT-3), and combined encoder-decoder types (like BART for translation).
▫️Key Components:
Residual connections (RC), layer normalization (LN), activation functions (AF), and position embeddings (PE) impact model performance. RCs stabilize, LN normalizes, AFs introduce non-linearity, and PE captures positional info.
3️⃣ Training at Scale
▫️Mixed precision training combines 32-bit and 16-bit data types to decrease memory usage and training time. Parallelization strategies like 3D parallelism distribute training across multiple computational resources, addressing memory constraints and communication issues.
▫️Zero Redundancy Optimizer (ZeRO) minimizes data redundancy across computational units during training. Techniques like checkpointing, weight decay, and gradient clipping help stabilize LLM training by managing model collapse, penalizing large parameters, and avoiding gradient explosion.
▫️Hyperparameters like batch size, learning rate, optimizer, dropout, and more impact model training. Dynamic strategies for batch size and learning rate are common in LLMs.
4️⃣ Evaluation
▫️Model development is a continual process involving iterative steps and evaluation to ensure satisfaction with the final product. Model evaluation, crucial in this process, assesses performance across various tasks, depending on the intended application.Benchmark Evaluation assess model performance using various benchmarks like ARC, HellaSwag, MMLU, and TruthfulQA across different domains and tasks.
#huggingface #openai #llm #languagemodels
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