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zakarth
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Model Details
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zakarth
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
The obvious approach doesn't work. Ask any strong LLM to "rewrite this in
Victorian English" and you get pastiche: invented archaisms (existeth,
avoucheth, attaineth), 47% length inflation, a general air of
Renaissance-fair. Recognisably old-sounding, not remotely period-accurate.
And the task can't be supervised directly, because paired (modern, Victorian) data does not exist. Nobody wrote the same paragraph twice, a century apart.
So the training data is manufactured backwards. Real Victorian paragraphs are the targets; a modern LLM rewrites them forward in time to produce the sources. Modernising is the easy direction — a strong model does it reliably. Training then runs in reverse, modern → Victorian, and the targets are never synthetic: every one is a real sentence by a real Victorian.
Two details make it work:
The evaluation instrument is the other half of the idea. Quality is scored
as perplexity under violet-1b4, a
1.4B model pretrained exclusively on Victorian text. It has never seen modern
English, so it discriminates period authenticity in a way no general-purpose
metric can — which is what makes the pastiche failure measurable rather than a
matter of taste.
600 period-compatible WikiText paragraphs, scored under violet-1b4.
| Violet PPL | anachronisms/1k | copy-ratio | length | |
|---|---|---|---|---|
| real Victorian text (floor) | 24.3 | — | — | — |
| Patina | 26.8 | 1.16 | 0.88 | 1.00 |
| Patina + DPO variant | 26.3 | 1.10 | 0.88 | 1.00 |
~85% of the distance from modern English to genuine Victorian, at a 1% confabulation rate and no length inflation.
Read the last row carefully, because it is the whole argument. Naive prompting matches the real-Victorian perplexity floor exactly — by padding 47% and inventing archaisms. Perplexity alone is necessary and nowhere near sufficient, which is why anachronism rate and length ratio sit beside it.
On ordinary narrative prose Patina does better than the table implies: the showcase examples above have a median perplexity of 15.7, below the real-Victorian floor. The 26.8 figure is dragged up by WikiText's density of modern proper nouns, which fidelity requires preserving.
Transformation depth (1 − similarity(input, output)) on held-out pairs:
| input register | register-naive ablation | Patina |
|---|---|---|
| fandom | 0.01 | 0.53 |
| rapper | 0.03 | 0.47 |
| slang | 0.23 | 0.39 |
| tumblr | 0.06 | 0.35 |
| casual | 0.25 | 0.38 |
| formal |
Trained on formal modernisations alone, the model copies input far from its training register — 0.01 depth means output equals input. Multi-register training fixes that across the board without disturbing formal performance.
Intended downstream use is corpus generation — manufacturing period-consistent pretraining data ("VictorianWikiText") for larger 19th-century models, since the supply of genuine clean period text is largely exhausted. Patina is the bootstrapper, not the destination.
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B", device_map="auto")model = PeftModel.from_pretrained(base, "zakarth/patina")tok = AutoTokenizer.from_pretrained("zakarth/patina")INSTR = ("Rewrite the following passage as authentic Victorian-era ""(19th-century) English prose. Preserve every fact and detail; ""change only the style and register.\n\n")msgs = [{"role": "user", "content": INSTR + your_text}]prompt = tok.apply_chat_template(msgs, tokenize=False,add_generation_prompt=True,enable_thinking=False)out = model.generate(**tok(prompt, return_tensors="pt").to(model.device),max_new_tokens=400, do_sample=True,temperature=1.0, top_p=0.9, top_k=40)
For verse, swap the instruction: "Rewrite the following piece as authentic Victorian-era (19th-century) English verse. Preserve every image, fact, and detail; change only the style and form."
A single sample from the snippet above will be noticeably more conservative than the showcase examples. That is not cherry-picking — it is the documented behaviour in §2 below, and the examples were produced by a selection procedure, which is the intended way to use this model. To reproduce them:
Sampling parameters, exactly as used:
| candidates per passage | k = 16 |
| temperature | 1.0 |
| top_p / top_k | 0.9 / 40 |
| max_new_tokens | max(220, 2.2 × source words) |
| passages | rendered independently, no cross-passage context |
Then select among the 16, in this order:
violet_ppl(candidate) / violet_ppl(source), lowest
wins. The ratio matters: raw Violet perplexity is domain-dependent (~27 on
plain narrative, ~280 on jargon-dense text), so raw values aren't comparable
between passages.The showcase passages scored a median relative perplexity of 0.12 — roughly 8× more period-plausible than their own inputs — at a median transformation depth of 0.55, above the 0.39 median of the training targets themselves. All six passed the fact check.
The single biggest failure mode, and it looks like the model is broken when it isn't.
Given a title and byline attached to a body, the model correctly identifies its input as a quoted document and applies the period convention — introduce it editorially, then reproduce it verbatim. Victorian books genuinely do treat quoted material that way. The result is a near-perfect copy.
| input | transformation depth |
|---|---|
| document with title + byline | 0.03 (copied) |
| identical text, front matter removed | 0.85 |
Strip headings, render the body, reattach headings separately.
Good renderings are roughly the top 25% of the sample distribution. Default behaviour is conservative because copying is always the locally safe token path — classic exposure bias — so a single greedy generation under-transforms.
Draw k candidates and select: 0.75^k of passages will have nothing good to
choose from. k=6 → 18% disappoint, k=12 → 3%, k=16 → 1%. If quality
feels erratic, this is almost always why.
The most period-perfect output is frequently the least true. From the same showcase run — this candidate scored perplexity 9.5, the best of all six, far below genuine Victorian text:
markdown
IN The algorithm keeps recommending me videos of people restoring rustedold tools, and I have to admit I am not even slightly mad about it.OUT "You may ask, my dear fellow, how I like it. I don't. I tell you I don't.I'm not a bit mad about it.
Flawless period voice. It also inverted the meaning — the source is fond of the videos, the output is not — and it passed an LLM fact-checker.
Elsewhere the same pipeline invented a recipient's name for a letter that had
none, misread 20:33 as "twenty minutes and thirty-three seconds", and — given
"the on-call engineer was on a train through a tunnel with no signal" —
produced a fully hallucinated Victorian railway disaster with a telegraph office
21 miles distant, at a perplexity better than real period text.
Ranking candidates by fluency selects the most convincing fabrication. Gate on facts before ranking on style, and never let a style score overrule a failed fact check.
zakarth/violet-1b4 — the
Victorian-only model used as the authenticity metriccroqaz/vintage-words| register-naive ablation | 27.4 | 1.98 | 0.88 | 1.00 |
| raw modern WikiText (ceiling) | 39.3 | — | 1.00 | 1.00 |
| naive "write it Victorian" prompt | 24.3 | 2.28 | 0.56 | 1.47 |
| 0.18 |
| 0.18 (unchanged — no forgetting) |