LogitBias

LogitBias lets you add a per-token offset to the model’s logits before sampling. Positive values favor a token; large negative values effectively ban it.

Quick reference
Type Dictionary<int, float> (token ID → bias)
Default Empty dictionary (no biases)
Range Typical -100.0 to +10.0
Category Fine-grained control
Field on SamplerParameters.LogitBias

What it does

Before any filter runs, for each token ID present in LogitBias, the engine adds the associated float to that token’s logit.

  • LogitBias[1234] = +5.0 — the token with ID 1234 gets a +5 boost to its logit. Strongly favors it.
  • LogitBias[5678] = -100.0 — the token at ID 5678 gets a massive negative bias. Effectively banned.
  • LogitBias[42] = -2.0 — token 42 is discouraged but still available.

Bias is applied once per step regardless of token occurrence history. It does not interact with the penalty window.

When to change it

Scenario Example
Ban profanity / slurs token IDs Set to -100.0
Force specific token (end-of-message, for instance) Set to +5.0
Soft discouragement without ban Set to -2.0

Obtaining token IDs requires the model’s tokenizer — not exposed in this parameter bag in the current SDK version. If you need logit-bias control, collect token IDs by tokenizing your target strings externally (for example, with the llama.cpp tokenize tool against the same model).

Example

var preset = new Qwen25Preset();

// Soft discourage token ID 1234; strong discourage 5678.
preset.SamplerParameters.LogitBias[1234] = -2.0f;
preset.SamplerParameters.LogitBias[5678] = -100.0f;

using var api = AsposeLLMApi.Create(preset);

Interactions

  • Temperature, other filters — run after LogitBias is applied.
  • Mirostat — LogitBias still applies; Mirostat adapts around the biased distribution.

What’s next

MirostatEta EnableInfill