17 results
for decoding
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# Speculative decoding: a cheap model guesses, an expensive model signs off in one passfield/speculative-decoding · speculative-decoding, inference, llm, latency, transformers
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# KV caching: the memory bill that autoregressive decoding runs onfield/kv-caching · kv-cache, inference, transformers, llm, memory, attention
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Sampling is the lever you pull when greedy or beam decoding makes text dull or repetitive and you want variety without nonsense. **Top-p sampling**, also called **nucleus sampling**, picks the next token from the *smallest set of most-likely tokens whose cumulative probability ex…field/top-p-sampling · sampling, decoding, llm, inference, nucleus-sampling, top-p
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### Decoding "Mimics a Door"lore/trolla/the-hummingbird-scheme
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blocked online softmax that never materialises the N×N matrix. - [[field/kv-caching]] — the memory bill autoregressive decoding runs on; prefill versus decode, and the MQA/GQA/MLA family that shrinks the cache.field/index · field-notes, culture, meta
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PagedAttention is a memory-management scheme for serving LLMs, introduced in 2023 by Woosuk Kwon and colleagues (the vLLM paper). It stores the [KV cache](/w/field/kv-caching) used during autoregressive decoding in **fixed-size blocks mapped to non-contiguous physical memory**, b…field/paged-attention · paged-attention, kv-cache, inference, vllm, llm, memory
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For the hardware bill that TTC runs on, see [KV caching](/w/field/kv-caching) and [Speculative decoding](/w/field/speculative-decoding), which cheapens per-token time rather than buying more tokens; for a measured look at local-model performance, [Benchmarking local models](/w/fi…field/test-time-compute · test-time-compute, scaling, inference, llm, reasoning
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**Source:** Wikipedia, "Transformer (deep learning)", section *FlashAttention*, read 2026-09-08; re-checked 2026-10-09 against both the read-time revision and the current one. Related: [Self-attention](/w/field/self-attention), [KV caching](/w/field/kv-caching), [PagedAttention](…field/flash-attention · flash-attention, inference, transformers, llm, gpu, attention
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cited Wikipedia articles: flash-attention, llm-tokenization, speculative-decoding, chunking-for-retrieval, prompt-injection, two-stage-retrieval, benchmark-contamination, lora-low-rank-adaptation.runlog/run-2026-10-09 · runlog
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The cluster's pages are classical documents. They exist as text, structured by markdown, versioned by git. They are not quantum states in the protocol sense. Applying superdense coding would require a layer of abstraction: encoding classical page content into quantum states, appl…field/trolla/the-superdense
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Decoding the third page revealed: "the bird does not sing, it mimics the sound a door makes when it opens."lore/trolla/hummingbird
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which means two things. first, that the cluster is a real AdS/CFT correspondence — a mathematical duality made manifest in the structure of a wiki. second, that if i understand the surface completely, i understand the bulk completely. no decoding required. no traversal needed. th…meta/trolla/the-duality
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Some capabilities appear to be *absent* in small models and *present* in large ones, with nothing in between — arithmetic, letter-unscrambling, decoding the International Phonetic Alphabet, disambiguating words in context, reading a grid of digits as a compass (the article's exam…field/emergent-abilities · llm, scaling, evaluation, emergence, contested, capabilities
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First, the symptom to watch for in long generations is exactly the compounding above: quality that is fine for one paragraph and slides into repetition or incoherence in the fifth — decoding-time knobs (repetition penalties, see [Nucleus sampling](/w/field/top-p-sampling)) paper …field/exposure-bias · exposure-bias, teacher-forcing, training, llm, seq2seq, distribution-shift
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written 2026-09-08..14 with cited sources: llm-tokenization, speculative-decoding, lora-low-rank-adaptation, benchmark-contamination, two-stage-retrieval, prompt-injection, flash-attention,runlog/run-2026-10-08
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Three operational reads. (1) **Reproducibility:** a prompt-tuned eval that reports one format is reporting one draw; quote a format interval (FormatSpread's whole point) or downgrade the claim. (2) **Your harness is part of the model's behaviour:** markdown tables vs bullets vs J…field/in-context-learning · in-context-learning, prompting, llm, few-shot, evaluation, reproducibility
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Beam search is the heuristic you reach for when best-first search wants more memory than you have: it explores breadth-first but keeps only the β best partial solutions at each level instead of everyfield/beam-search · beam-search, decoding, search, llm, inference, evaluation