An alternative to the Transformer architecture that processes sequences with linear O(n) complexity instead of quadratic O(n^2) attention, enabling efficient handling of very long sequences. Mamba introduced selective state spaces where the model dynamically filters information based on content. Hybrid architectures like Jamba combine SSM efficiency with attention's retrieval strength.
Empieza por la explicación más corta y útil antes de profundizar.
An alternative to the Transformer architecture that processes sequences with linear O(n) complexity instead of quadratic O(n^2) attention, enabling efficient handling of very long sequences. Mamba introduced selective state spaces where the model dynamically filters information based on content. Hybrid architectures like Jamba combine SSM efficiency with attention's retrieval strength.
Usa primero la analogía corta para razonar mejor sobre el término cuando aparezca en código, docs o prompts.
Piensa en esto como una pieza de la pila de contexto o inferencia usada en productos con agentes o LLMs.
Ubica el término dentro de la capa de Solana en la que vive para razonar mejor sobre él.
LLMs, RAG, embeddings, inferencia y primitivas orientadas a agentes.
Convierte el término de vocabulario en algo operacional para producto e ingeniería.
Este término desbloquea conceptos adyacentes rápido, así que funciona mejor cuando lo tratas como un punto de conexión y no como una definición aislada.
Usa este bloque compacto cuando quieras dar contexto sólido a un agente o asistente sin volcar toda la página.
State Space Model (Mamba) (state-space-model) Categoría: IA / ML Definición: An alternative to the Transformer architecture that processes sequences with linear O(n) complexity instead of quadratic O(n^2) attention, enabling efficient handling of very long sequences. Mamba introduced selective state spaces where the model dynamically filters information based on content. Hybrid architectures like Jamba combine SSM efficiency with attention's retrieval strength. Aliases: SSM, Mamba Relacionados: Transformer, Mixture of Experts (MoE), Attention Mechanism
Usa contexto del glosario, relaciones entre términos, modelos mentales y builder paths para recibir respuestas estructuradas en vez de output genérico.
Opcional: pega código Anchor, Solana o Rust para que el Copilot mapee primitivas de vuelta al glosario.
El Copilot responderá usando el término actual, conceptos relacionados, modelos mentales y el grafo alrededor del glosario.
Estas ramas muestran qué conceptos toca este término directamente y qué existe una capa más allá de ellos.
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 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.
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.
Estos son los siguientes conceptos que vale la pena abrir si quieres que este término tenga más sentido dentro de un workflow real de Solana.
Estas entradas son fáciles de mezclar cuando lees rápido, haces prompting a un LLM o estás entrando en una nueva capa de Solana.
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 generative AI architecture that creates images, video, or audio by learning to reverse a noise-adding process—starting from pure noise and iteratively denoising to produce coherent output. Diffusion models power leading image generators (Stable Diffusion, DALL-E 3, Midjourney) and video generators (Sora). Key variants include latent diffusion (operating in compressed space) and diffusion transformers (DiT).
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.
Las entradas del glosario se vuelven útiles cuando están conectadas. Estos enlaces son el camino más corto hacia ideas adyacentes.
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 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.
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.
Estas entradas viven junto al término actual y ayudan a que la página se sienta parte de un grafo de conocimiento más amplio en lugar de un callejón sin salida.
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.