Description
Return a token's competition rank and next-distinct probability margin.
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 a token's competition rank and next-distinct probability margin.
When to use this tool
Use Token Probability Rank Margin when checking the stated model-dimension, ranking, probability-prefix or local transform convention.
How to use the tool and how it works
- Enter probabilities, tokenIndex. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read rank, marginToNext, hasNext. Inspect hasNext before interpreting the associated values.
Return a token's competition rank and next-distinct probability margin. Require nonempty values in [0,1] and valid tokenIndex; rank = one plus count strictly greater, margin subtracts largest strictly smaller value or is zero with hasNext false.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| probabilities | Probabilities | Required. Array of finite number; maximum items 256. |
| tokenIndex | Token Index | Required. Safe integer; minimum 0. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| rank | One plus number strictly greater than the selected token; tied values share rank. | Required. Safe integer. |
| marginToNext | Gap to largest strictly smaller value; zero placeholder when hasNext=false. | Required. Finite number. |
| hasNext | Has Next | Required. Boolean. |
{
"input": {
"probabilities": [
0.5,
0.3,
0.2
],
"tokenIndex": 1
},
"output": {
"rank": 2,
"marginToNext": 0.09999999999999998,
"hasNext": true
}
}Limitations and assumptions
- The vector need not sum to one and equal-score tokens share rank; no calibrated probability, sampling or model quality is established.
- 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.
- Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Numeric signed-zero results are exported as zero.
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
Similar or alternative tools
- Artificial Intelligence: Topk Boundary Tie Policy
Return top-k score indexes with a boundary-tie policy.
- Token Logit Scaling Gap Tool
Inspect one explicit temperature-scaled logit; no probability normalization or model call.