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.
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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.
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Mixture of Experts (MoE) (mixture-of-experts) Category: AI / ML Definition: 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. Aliases: MoE, Sparse MoE Related: Transformer, State Space Model (Mamba), LLM (Large Language Model)
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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).
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.
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.
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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).
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.
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.
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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.