chartreuse-verte/prose-rewriter-1.7b-v2.1

prose-rewriter-1.7b-v2.1

A paragraph-level prose rewriter: it takes prose written by a large model and re-renders it to be more human, preserving the semantics it was given.

Qwen/Qwen3-1.7B-Base with a rank-32 LoRA merged in at strength 1.10.

Invented people, mostly gone. v2 would would invent a person where there was none -- She offered a trembling smile -> She gave him a trembling smile. Training pairs that teach this are now rejected: the input names one gender and the target brings in the other. On 400 single-gender roleplay paragraphs the rate falls from 1.3% to 0.4%.

Fixed dialogues randomly dropping double-quotes.

More published fiction. Professionally edited novels are now 40% of the target side, up from 33%.

Variants

The quant carries the chat template and stops on <|im_end|>, and the adapted output head is kept separate from the token embeddings, stored at Q8_0.

Prompt format

<|im_start|>source
{paragraph}<|im_end|>
<|im_start|>rewrite

The chat template in this repo builds exactly that string, byte for byte, from one message:

messages = [{"role": "source", "content": paragraph}]
tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

It is not a chat model. Every message is rendered as the source paragraph, so a runtime that probes the template with a user message still gets a valid prompt. Send one message per call.

Serving recipe

Sampled at temperature=0.9, top_p=0.9. Temperature 0.9 has been tested internally to be the most optimal value. It's recommended you use this.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "chartreuse-verte/prose-rewriter-1.7b-v2.1"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="cuda").eval()

def rewrite(paragraph):
    text = tok.apply_chat_template(
        [{"role": "source", "content": paragraph}],
        tokenize=False, add_generation_prompt=True,
    )
    ids = tok(text, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
    out = model.generate(ids, max_new_tokens=512, do_sample=True, temperature=0.9, top_p=0.9)
    return tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True).strip()

Same thing under llama.cpp. The role is source, which no chat API models, so build the string yourself; <|im_end|> stops it:

llama-cli -m GGUF/prose-rewriter-1.7b-v2.1-Q8_0.gguf -no-cnv -n 512 --temp 0.9 --top-p 0.9 \
  -p '<|im_start|>source
{paragraph}<|im_end|>
<|im_start|>rewrite
'

Input length

The training pool's median input is 37 words and 80% of it is under 70, so serve it on anything from a full sentence up.

The practical floor is about 15 words. Below it the failure mode is padding, cutting and fabrication rather than gibberish. Below 80 bytes, pass the text through unchanged.

Evaluation

Both releases measured as they ship, baked at strength 1.10, on 365 held-out paragraphs of LLM-written prose that neither model saw in training, at temperature=0.9, top_p=0.95, three swipes each. 1,095 generations an arm.

Pronoun invention numbers for this release:

Paired over the 400 inputs, the phantom pronoun falls (t = -2.7) and invented proper nouns rise (t = +2.3).

Structural, on the 365 paragraphs:

Paired over the 303 inputs of 51-90 words, no column separates the two.

Unsupported content -- a sentence in the rewrite the input does not entail, scored by NLI with the input alone as the premise:

The register numbers on the same paragraphs, against the input:

Paired over all 365 inputs, none of the three separate.

Dialogue: the number of spoken lines changes on 2.5% of rewrites against v2's 3.2%, and of the rewrites whose input carried quoted speech, 2.3% lost the quotes entirely -- the same for both.

Markup round-trip on 365 tagged paragraphs -- every marker handed back in order, with the same counts: 70.1% for v2, 71.2% here.

A note on strength

Every number above is measured at the strength the release is baked at, which is not the same as measuring the adapter. LoRA strength is spent at merge time (W + (B @ A) * 2.20 here), and the metrics move with it: read at the adapter's natural 1.0 this same checkpoint is a materially different artifact.

1.10 is the lowest strength at which the pass-through and near-verbatim rates bottomed out on the strength sweep.

Training

Corrupt forward, train backward. The human paragraph is the target; an on-policy LLM manufactures the input by slop-ifying it.

The target side is human prose: published contemporary literary fiction, r/WritingPrompts (`Mollymo/Human-to-AI-writing`), AO3 (`midwestern-simulation-active/ao3_random_subset`), a scrape of bluemoonroleplaying.com -- the only human writing in the pool already in the deployment's own register -- and a sliver of fanfiction.net (`atom-in-the-universe/fanfics-10k-10k`).

The input side is manufactured from those targets, weighted and share-capped so that no single generator's tics dominate:

Pairs pass invariant gates before they reach the GPU: POV, tense, who is in the scene, the gender of who is in the scene, the number of spoken lines, grammatical correctness on the target side, content recall stratified by target length, and NLI entailment both ways. Two further screens shape what reaches training -- a floor on how much a pair actually changes, and a floor on the sentence-length variety of the target, applied at a higher threshold for long paragraphs than short ones.

Loss on the target paragraph only. Everything before rewrite is masked.

The merge

Merged at strength 1.10. Rank 32 with alpha 64 is a LoRA scaling of 2.0, so the effective scaling is 2.20: W + (B @ A) * 2.20. Merged in float32, stored bfloat16.

lm_head is adapted, and Qwen3-1.7B-Base ties lm_head.weight to embed_tokens.weight. This checkpoint is untied: the merged output head is stored separately and the input embeddings are bit-identical to the base model's, which is what training assumed. config.json says tie_word_embeddings: false and it means it. Do not re-tie it, and if you convert to another format, check that the head survived.

Limitations

• —Not an instruct model. It has one job and one prompt. There is nothing to ask it.
• —Works on fictional prose only. May not work on technical documentation.
• —One paragraph per call. Longer input degrades; split it.
• —Still invents people occasionally. The phantom pronoun is down, not gone, and invented proper nouns went up. Check the output when the cast matters.
• —Truncates. It drops below 0.75x the input's length on 19.2% of paragraphs. Check the output when length matters.
• —Will not pass AI detectors. Pangram and such will still know because this model preserves word choices and certain sentence structures.
• —English only, narrative register (third and first person fiction, dialogue with quoted speech).
• —Short input pads, cuts and invents. The floor is about 15 words. See Input length.
• —Repeats a noun sooner than an LLM would. Human prose reuses a plain noun where generated prose uses a synonym, and this model has learned that habit.

License

The weights in this repository are released under the GNU Affero General Public License, version 3. The full text is in LICENSE.

This is a derivative of `Qwen/Qwen3-1.7B-Base`, which is licensed under Apache License 2.0. That license is preserved and its terms continue to apply to the base weights this model was built from; the AGPL covers the combined work as distributed here. Apache-2.0 is one-way compatible with AGPLv3, which is what makes this combination possible.

If you run a modified version of this model as a network service, AGPL section 13 requires you to offer the corresponding source of your modifications to its users.