Llama line break remover

This tool takes Llama output that pastes with a hard break after nearly every sentence or list item and joins it into a single flowing block. Every line break is replaced with a single space, resulting runs of spaces are collapsed, and the output is one continuous paragraph, regardless of which interface produced the input.

Processing runs client-side, in JavaScript, entirely within your browser. There is no upload and no server round trip, and accordingly no run limit.

Llama is a model family rather than a single hosted product, so a given answer can arrive through a self-hosted web UI, a desktop application, a hosted playground, or a custom application built on the model's API. Each wraps the raw output in its own markdown renderer, and each makes an independent decision about how a list or a step-by-step answer becomes line breaks.

The identical underlying answer can therefore paste as one flowing paragraph from one interface and as a fragment-per-line block from another, a function of the rendering layer rather than the model's own output.

Submitting an identical query through a minimal self-hosted interface and a polished chat application built on the same underlying model can produce two visibly distinct pastes: one a single flowing paragraph, the other a numbered list with a hard break after each point, despite equivalent underlying reasoning.

Neither rendering is incorrect. They represent two independent presentation choices layered atop one answer, and this tool addresses the case where the resulting structure does not match what the task requires.

No configuration required.

Step 1

Paste the reply from whichever Llama interface produced it into the input field.

Step 2

Run the join. It executes instantly, locally.

Step 3

Read the result, one continuous paragraph.

Every line break in the input, including a deliberate paragraph break, is replaced with a single space, producing one unbroken paragraph, the correct behaviour for text that should not have had breaks at all.

Where genuine paragraph structure exists and must be preserved, run this and manually reinsert a blank line at each intended paragraph start, since this tool does not distinguish an intentional break from an unwanted one.

Text with one sentence per line, pasted into an email or a CMS field, reads as an unintended list, and rich-text editors frequently reflow it in unexpected ways. A caption or meta description built from such text can truncate at the first break rather than the actual limit.

In code and data, a stray hard break inside a field expecting one continuous value, a CSV cell, a JSON string, breaks the format outright.

Passed into a translation tool or a segment count, fragmented text distorts the result: a single sentence broken across five lines can be counted as five separate short segments rather than the one sentence it constitutes.

A minimal self-hosted interface or a raw API playground is more likely to pass through the model's own line breaks with little modification. A polished chat application optimized for on-screen readability is more likely to reformat a list into one item per line regardless of the model's original structure.

In either case, the correction is identical: text carrying unwanted breaks is joined here, independent of the interface that produced it.

Whether the interface streams tokens live or waits for the complete response also plays a role. A streaming display can show a line break the instant the model emits a newline character, before any formatting cleanup pass has a chance to run, while a display that buffers the full response first can apply that cleanup before anything appears on screen. The same underlying answer can therefore arrive more fragmented from a streaming interface than a buffered one, independent of the prompt itself.

Anyone converting a Llama-generated list into a normal paragraph for an email, a product description or a caption uses this to eliminate the one-line-per-item structure before publishing.

Engineers building an application on a self-hosted Llama model use this during testing to inspect raw output as flowing text rather than as a rendering artefact of a particular template.