You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I am looking to analyze the impact of SGLang’s constraint-guided decoding (e.g., regex or JSON schema) on the model's output distribution. Currently, it is easy to see the final logprobs for the selected tokens, but it is difficult to see the "tension" between what the model originally predicted and what the constraint allowed.
Is there a recommended way or can a new feature be added to:
Raw Logprobs: The top-k logprobs generated by the model before any LogitProcessor or bitmask is applied.
Masked Logprobs: The logprobs after the constraint mask has been applied (setting illegal tokens to $-\infty$) [I assume this is what is already returned]
reacted with thumbs up emoji reacted with thumbs down emoji reacted with laugh emoji reacted with hooray emoji reacted with confused emoji reacted with heart emoji reacted with rocket emoji reacted with eyes emoji
Uh oh!
There was an error while loading. Please reload this page.
-
I am looking to analyze the impact of SGLang’s constraint-guided decoding (e.g., regex or JSON schema) on the model's output distribution. Currently, it is easy to see the final logprobs for the selected tokens, but it is difficult to see the "tension" between what the model originally predicted and what the constraint allowed.
Is there a recommended way or can a new feature be added to:
Raw Logprobs: The top-k logprobs generated by the model before any LogitProcessor or bitmask is applied.
Masked Logprobs: The logprobs after the constraint mask has been applied (setting illegal tokens to$-\infty$ ) [I assume this is what is already returned]
Beta Was this translation helpful? Give feedback.
All reactions