A technique for transferring capabilities from a large 'teacher' model to a smaller 'student' model, typically by having the teacher generate a synthetic dataset that the student is fine-tuned on. Distilled models can match or exceed teacher performance on specific tasks while being much cheaper to deploy. Common in 2024-2025 for creating efficient specialized models.
Comece pela explicação mais curta e útil antes de aprofundar.
A technique for transferring capabilities from a large 'teacher' model to a smaller 'student' model, typically by having the teacher generate a synthetic dataset that the student is fine-tuned on. Distilled models can match or exceed teacher performance on specific tasks while being much cheaper to deploy. Common in 2024-2025 for creating efficient specialized models.
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.
Knowledge Distillation (distillation) Categoria: IA / ML Definição: A technique for transferring capabilities from a large 'teacher' model to a smaller 'student' model, typically by having the teacher generate a synthetic dataset that the student is fine-tuned on. Distilled models can match or exceed teacher performance on specific tasks while being much cheaper to deploy. Common in 2024-2025 for creating efficient specialized models. Aliases: Model Distillation Relacionados: Synthetic Data (AI Training), Fine-Tuning, Quantization (AI)
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.
Artificially generated training data produced by LLMs or other AI models, used to augment or replace human-annotated datasets. Techniques include prompt-based generation, retrieval-augmented pipelines, and iterative self-refinement. Synthetic data slashes costs from $5-20 per human preference point to under $0.01 per sample and became central to post-training pipelines in 2024-2025.
The process of further training a pre-trained model on a specialized dataset to improve performance on specific tasks. Fine-tuning adapts a foundation model's weights using domain-specific data (e.g., Solana documentation, smart contract code). Techniques include full fine-tuning, LoRA (Low-Rank Adaptation), and QLoRA. Fine-tuned models can outperform general models on narrow tasks.
A model compression technique that reduces weight precision (e.g., from 16-bit to 4-bit) to decrease model size and inference cost while preserving most quality. Three dominant formats in 2024-2025: GGUF (flexible CPU/GPU format for llama.cpp), GPTQ (GPU-optimized post-training quantization), and AWQ (activation-aware weight quantization). All keep quality within ~6% of full-precision at 4-bit.
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.
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.
Entradas de glossário só ficam úteis quando estão conectadas. Esses links são o caminho mais curto para ideias adjacentes.
Artificially generated training data produced by LLMs or other AI models, used to augment or replace human-annotated datasets. Techniques include prompt-based generation, retrieval-augmented pipelines, and iterative self-refinement. Synthetic data slashes costs from $5-20 per human preference point to under $0.01 per sample and became central to post-training pipelines in 2024-2025.
The process of further training a pre-trained model on a specialized dataset to improve performance on specific tasks. Fine-tuning adapts a foundation model's weights using domain-specific data (e.g., Solana documentation, smart contract code). Techniques include full fine-tuning, LoRA (Low-Rank Adaptation), and QLoRA. Fine-tuned models can outperform general models on narrow tasks.
A model compression technique that reduces weight precision (e.g., from 16-bit to 4-bit) to decrease model size and inference cost while preserving most quality. Three dominant formats in 2024-2025: GGUF (flexible CPU/GPU format for llama.cpp), GPTQ (GPU-optimized post-training quantization), and AWQ (activation-aware weight quantization). All keep quality within ~6% of full-precision at 4-bit.
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.