A Chinese AI lab that released DeepSeek-R1 in January 2025, a 671B-parameter open-weight reasoning model achieving performance comparable to OpenAI's o1 at significantly lower cost. DeepSeek-R1 generates visible chain-of-thought reasoning using GRPO training and demonstrated that pure RL with verifiable rewards can produce emergent reasoning. DeepSeek-V3 uses a MoE architecture with ~37B active parameters.
Comece pela explicação mais curta e útil antes de aprofundar.
A Chinese AI lab that released DeepSeek-R1 in January 2025, a 671B-parameter open-weight reasoning model achieving performance comparable to OpenAI's o1 at significantly lower cost. DeepSeek-R1 generates visible chain-of-thought reasoning using GRPO training and demonstrated that pure RL with verifiable rewards can produce emergent reasoning. DeepSeek-V3 uses a MoE architecture with ~37B active parameters.
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.
DeepSeek (deepseek) Categoria: IA / ML Definição: A Chinese AI lab that released DeepSeek-R1 in January 2025, a 671B-parameter open-weight reasoning model achieving performance comparable to OpenAI's o1 at significantly lower cost. DeepSeek-R1 generates visible chain-of-thought reasoning using GRPO training and demonstrated that pure RL with verifiable rewards can produce emergent reasoning. DeepSeek-V3 uses a MoE architecture with ~37B active parameters. Aliases: DeepSeek-R1, DeepSeek-V3 Relacionados: Reasoning Model, Mixture of Experts (MoE), Open-Source AI Models
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 class of LLMs trained with reinforcement learning to generate step-by-step internal chain-of-thought before producing a final answer, enabling stronger performance on complex math, coding, and logic tasks. Pioneered by OpenAI's o1 (September 2024) and followed by o3, DeepSeek-R1, and Claude's extended thinking mode. Unlike standard LLMs that answer directly, reasoning models produce a variable-length internal CoT, allowing controllable compute at inference time.
A neural network architecture that routes each input to a subset of specialized 'expert' sub-networks rather than activating all parameters, dramatically improving efficiency. Only a fraction of total parameters are active per token (e.g., DeepSeek-V3 has 671B total but ~37B active). MoE enables training much larger models at manageable compute costs. Used in production models like Mixtral, Jamba, and DeepSeek-V3.
AI models with publicly released weights that can be downloaded, modified, and self-hosted. Notable open models: Llama 3 (Meta), Mistral, Falcon, Gemma (Google), Phi (Microsoft). Open models enable privacy (data stays local), customization (fine-tuning), and cost control. Trade-off: generally less capable than frontier proprietary models but rapidly improving.
Estes são os próximos conceitos que valem abrir se você quiser que este termo faça mais sentido dentro de um workflow real de Solana.
Entradas de glossário só ficam úteis quando estão conectadas. Esses links são o caminho mais curto para ideias adjacentes.
A class of LLMs trained with reinforcement learning to generate step-by-step internal chain-of-thought before producing a final answer, enabling stronger performance on complex math, coding, and logic tasks. Pioneered by OpenAI's o1 (September 2024) and followed by o3, DeepSeek-R1, and Claude's extended thinking mode. Unlike standard LLMs that answer directly, reasoning models produce a variable-length internal CoT, allowing controllable compute at inference time.
A neural network architecture that routes each input to a subset of specialized 'expert' sub-networks rather than activating all parameters, dramatically improving efficiency. Only a fraction of total parameters are active per token (e.g., DeepSeek-V3 has 671B total but ~37B active). MoE enables training much larger models at manageable compute costs. Used in production models like Mixtral, Jamba, and DeepSeek-V3.
AI models with publicly released weights that can be downloaded, modified, and self-hosted. Notable open models: Llama 3 (Meta), Mistral, Falcon, Gemma (Google), Phi (Microsoft). Open models enable privacy (data stays local), customization (fine-tuning), and cost control. Trade-off: generally less capable than frontier proprietary models but rapidly improving.
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.
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).
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.