ManySpace
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Yapay ZekâÖnerilenAIML-SYS-019

Speculative Decoding

A small draft model proposes tokens that the large model verifies in parallel, preserving the output distribution.

Bu konunun henüz Türkçe çevirisi yok, bu yüzden İngilizce gösteriliyor.

Zorluk
4 / 5
Önem
4 / 5
Çalışma süresi
1–2 sa
Çalışma türü
Karma

Neden önemli

The learner can vary the draft model's acceptance rate and predict the resulting speedup.

Bu konuya giden rota

Rota AIML-SYS-019
What Is a Model? Tasks: Regression, Classification, and Beyond Features and Targets Probabilistic Prediction Classification Framing Linear Regression I: Prediction and Residuals Embeddings Tokens and Tokenization Scores and Logits Loss and Fitting: Squared Error Similarity in Vector Space Multiclass Scores and Softmax The Artificial Neuron Autoregression Objective Functions and Empirical Risk Query, Key, Value Activation Functions Next-Token Prediction Training vs Serving Similarity Scores The Multilayer Perceptron Temperature Weighted Aggregation Latency and Throughput Forward Propagation Top-k and Top-p Sampling Self-Attention Memory and Model Size Context and Conditioning The Attention Block The Causal Transformer Context Windows Caching and Reuse KV Caching and Batched LLM Serving Speculative Decoding