Llama passive voice fixer
What the Llama passive voice fixer does
Paste in text drafted with a Llama-based model and this tool checks for a specific problem that's more likely with Llama than with a single closed model: inconsistent passive voice usage across one document, where one paragraph reads heavily passive and formal and the next reads blunt and active, because different sections may trace back to different fine-tunes or settings. It fixes the inconsistency and leaves correctly used passive voice alone.
It runs on our AI server rather than in your browser, since judging which passive sentences need fixing takes real reading, not a mechanical find-and-replace. You get 5 free runs every rolling 24 hours, no account and no signup required to start.
The problem
Why passive voice usage varies within a single Llama-drafted document
Because Llama is open-weight, a document assembled from more than one session, or from a model that shifted behavior across a long generation, can carry two different defaults: one part heavily hedged and passive, in the style some instruction-tuned fine-tunes lean toward, and another part blunt and active, in the style a differently tuned or differently sampled pass produces.
Neither default is wrong on its own. The problem is having both in the same document without a reason, which reads as inconsistent rather than as a deliberate stylistic choice. The actual issue to fix is narrower than passive voice itself: it's agentless passive that drops responsibility, and passive used inconsistently rather than by any real editorial choice.
How it works
How to fix passive voice in Llama text in three steps
Paste, run, copy. Nothing to configure first.
- Step 1
- Paste the Llama draft into the box, whole document or a section at a time.
- Step 2
- Press Fix. The text goes to our AI server, which finds passive constructions and evens out inconsistent usage across the passage.
- Step 3
- Read the result against your original. Sentences where passive voice is the right choice for the context are left as they were.
Why it matters
What inconsistent passive usage actually costs a document
A document that shifts between heavily hedged, passive prose and blunt, active statements reads as if two different people wrote it, which undercuts the sense of one coherent voice even when the underlying facts are all correct.
The agentless version costs more where it appears: a sentence that quietly drops who did something reads as evasive whether or not that was the intent, especially when the surrounding paragraphs were confidently active.
The actual test
When passive voice is the right call, regardless of which fine-tune wrote it
Passive voice is correct, not just tolerable, in a specific set of situations, and the fixer is built to recognise them rather than flatten every passive sentence into active voice or force every active sentence into passive.
The test is whether the agent is unknown, irrelevant to the point, or being de-emphasised on purpose because the action or the result matters more than who did it. Scientific writing leans on passive constructions for exactly this reason, and that convention holds regardless of which Llama deployment or fine-tune wrote the sentence.
The failure mode isn't passive voice itself, it's agentless passive dodging a responsible party, and inconsistent usage that tracks which part of the model pipeline wrote which paragraph rather than any actual editorial reasoning.
Straight answer
Does this make Llama text undetectable?
No, and fixing passive voice consistency was never going to do that on its own. We don't test this tool against AI detectors and we don't tune it to beat one, so we have no basis to promise what a detector will conclude about text you've run through it.
What it reliably does is bring one document to one consistent standard for passive voice, correcting agentless passive that dodges attribution along the way. That's a real improvement in how the writing reads, independent of any claim about detection.
In practice
Who uses the Llama passive voice fixer, and when
Developers and hobbyists running local Llama-based models who noticed a document reads inconsistently, some paragraphs hedged and passive, others blunt and active, especially after combining output from more than one session or fine-tune.
Writers and editors cleaning up a Llama-assisted draft who want one consistent standard applied across the whole document rather than whatever each paragraph happened to inherit.