The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5).
Comece pela explicação mais curta e útil antes de aprofundar.
The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5).
Use primeiro a analogia curta para raciocinar melhor sobre o termo quando ele aparecer em código, docs ou prompts.
Pense nisso como uma peça da pilha de contexto ou inferência usada em produtos com agentes ou LLMs.
Coloque o termo dentro da camada de Solana em que ele vive para raciocinar melhor sobre ele.
LLMs, RAG, embeddings, inferência e primitivas voltadas a agentes.
Transforme o termo de vocabulário em algo operacional para produto e engenharia.
Este termo destrava conceitos adjacentes rapidamente, então funciona melhor quando você o trata como um ponto de conexão, não como definição isolada.
Use este bloco compacto quando quiser dar contexto aterrado para um agente ou assistente sem despejar a página inteira.
Transformer (transformer) Categoria: IA / ML Definição: The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5). Relacionados: LLM (Modelo de Linguagem Grande), Attention Mechanism
Use contexto do glossário, relações entre termos, modelos mentais e builder paths para receber respostas estruturadas em vez de output genérico.
Opcional: cole código Anchor, Solana ou Rust para o Copilot mapear primitivas de volta para termos do glossário.
O Copilot vai responder usando o termo atual, conceitos relacionados, modelos mentais e o grafo ao redor do glossário.
Esses ramos mostram quais conceitos esse termo toca diretamente e o que existe uma camada além deles.
A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.
A neural network component that allows models to weigh the relevance of different parts of the input when producing output. Self-attention computes query-key-value dot products across all positions, enabling each token to 'attend' to every other token. Multi-head attention runs multiple attention functions in parallel. Attention is O(n²) in sequence length, driving context window research.
Estes são os próximos conceitos que valem abrir se você quiser que este termo faça mais sentido dentro de um workflow real de Solana.
Essas entradas são fáceis de misturar quando você lê rápido, faz prompting em um LLM ou está entrando em uma nova camada de Solana.
The economic design of a cryptocurrency token: supply schedule, distribution, utility, incentive mechanisms, and value accrual. Key parameters: total/circulating supply, inflation/deflation, vesting schedules, staking rewards, fee burning, and governance rights. Good tokenomics aligns incentives between users, developers, and token holders. AI tools increasingly help analyze tokenomics models.
Entradas de glossário só ficam úteis quando estão conectadas. Esses links são o caminho mais curto para ideias adjacentes.
A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.
A neural network component that allows models to weigh the relevance of different parts of the input when producing output. Self-attention computes query-key-value dot products across all positions, enabling each token to 'attend' to every other token. Multi-head attention runs multiple attention functions in parallel. Attention is O(n²) in sequence length, driving context window research.
Essas entradas vivem ao lado do termo atual e ajudam a página a parecer parte de um grafo maior, não um beco sem saída.
A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.
A neural network component that allows models to weigh the relevance of different parts of the input when producing output. Self-attention computes query-key-value dot products across all positions, enabling each token to 'attend' to every other token. Multi-head attention runs multiple attention functions in parallel. Attention is O(n²) in sequence length, driving context window research.
A large AI model trained on broad data that can be adapted for many downstream tasks. Foundation models (GPT-4, Claude, Llama 3, Gemini) are pre-trained on internet-scale text/code and can be fine-tuned, prompted, or used via APIs for specific applications. The term emphasizes that one base model serves as the foundation for diverse use cases rather than training task-specific models.
The practice of crafting input text (prompts) to guide LLM behavior and output quality. Techniques include: zero-shot (direct instruction), few-shot (providing examples), chain-of-thought (step-by-step reasoning), system prompts (setting context/persona), and structured output formatting. Effective prompts are specific, provide context, and include constraints. Critical for AI-assisted blockchain development.