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Speculative Decoding Prefix Accept Count Tool

Count only the consecutive accepted draft prefix; later flags do not bypass a rejection.

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Description

Return accepted prefix length from supplied speculative-decoding flags.

Language-model bookkeeping separates scores, probabilities, dimensions, capacity and positional transforms. A local ranking, padding or coordinate operation can illustrate a model component without running a model, sampling a token, fitting calibration or demonstrating output quality.

Return accepted prefix length from supplied speculative-decoding flags.

When to use this tool

Use Speculative Decoding Prefix Accept Count when checking the stated model-dimension, ranking, probability-prefix or local transform convention.

How to use the tool and how it works

  1. Enter accepted. Use the units and conventions below.
  2. Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
  3. Read acceptedPrefix, firstRejected. Use the output convention below.

Return accepted prefix length from supplied speculative-decoding flags. acceptedPrefix is the first false index or array length when all accepted; firstRejected returns -1 when none.1

Input fieldMeaning and unitsAccepted input
acceptedAcceptedRequired. Array of boolean; maximum items 256.
Output fieldInterpretationReturned shape
acceptedPrefixAccepted PrefixRequired. Safe integer.
firstRejectedFirst false index, or -1 sentinel when every supplied acceptance flag is true.Required. Safe integer.
json
{
  "input": {
    "accepted": [
      true,
      true,
      false,
      true
    ]
  },
  "output": {
    "acceptedPrefix": 2,
    "firstRejected": 2
  }
}

Limitations and assumptions

  • Flags are supplied rather than obtained from a target model; no acceptance probability, token validation or speculative decoding is executed.
  • Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.

Alternative or Complementary methods

Use a complete implementation with verified tensor/token conventions and appropriate model evaluation when actual inference, memory, sampling or quality results are required.

References

  1. Calculation and input contract: artificial-intelligence/speculative-decoding-prefix-accept-count — tool.io

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