Start with a low or zero temperature, the correct chat template for your model, and a prompt that explicitly protects meaning, voice, terminology, and formatting. Keep top_p, top_k, and repetition controls at their defaults until you have a reason to change them. There is no proven universal setting for copyediting: runtimes have different defaults, and the available documentation describes generation controls rather than grammar-editing quality.
Recommended starting settings for local LLM copyediting
For restrained corrections, first reduce randomness if your model and runtime support it. Leave other sampling controls unchanged, confirm the model’s intended chat template, and make sure the entire source passage fits within the context and output limits. These are practical starting points to test—not a guaranteed recipe for better edits.
| Setting | What it controls | Copyediting starting point |
|---|---|---|
| Temperature | How much randomness influences token selection. The llama.cpp server documentation lists 0.8 as its default; LocalAI’s common parameter table lists 0.9. llama.cpp server documentation; LocalAI model configuration. | Try a low value or zero if edits are too creative and the runtime accepts it. Check results rather than assuming zero guarantees identical or correct edits across implementations. |
| top_p and top_k | Limit the pool of candidate next tokens. llama.cpp lists defaults of 0.95 for top_p and 40 for top_k. llama.cpp server documentation. | Keep the defaults initially. Avoid sharply restricting both while also changing temperature, so you can tell which change affected the output. |
| Repetition penalty and repeat_last_n | Control repeated token sequences. llama.cpp lists defaults of 1.1 for repeat_penalty and 64 for repeat_last_n. llama.cpp server documentation. | Start with the runtime defaults; adjust gently only if the model loops or repeats. A strong penalty can also alter ordinary repeated words. |
| Frequency and presence penalties | Influence repetition and diversity. llama.cpp lists both as disabled by default at 0.0; LocalAI lists a supported range from -2 to 2. llama.cpp server documentation; LocalAI model configuration. | Leave them neutral unless you observe a specific repetition problem. They are not established grammar correctors. |
| Context and output limits | Determine how much input the model can consider and how much text it can return. LocalAI documents configurable context size and max_tokens behavior. LocalAI model configuration. | Allow enough context for the passage and enough output capacity for the complete edited text. Check for truncation. |
| Seed | Can help control randomness for comparisons when supported. llama.cpp documents a random default of -1. llama.cpp server documentation. | Fix a seed when you need repeatable comparisons, then verify how your runtime handles it. |
Defaults are not quality recommendations: llama.cpp and LocalAI document different temperature defaults, so inspect the configuration for your own runner and model.
Use a prompt that limits the edit
Sampling settings cannot tell a model which edits are acceptable. State the task and the boundaries directly. This prompt is a practical suggestion, not a tested or source-validated optimum:
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Correct grammar, spelling, punctuation, and clear wording errors in the text below. Preserve the author’s meaning, voice, terminology, and formatting. Do not add facts, examples, claims, or explanations. If a sentence is ambiguous and changing it could alter its meaning, leave it unchanged and mark it for review. Return only the edited text.
For consequential writing, ask for a separate change list or compare the original with the result so you can review edits that may affect meaning.
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Choose the right model template and fit the text
Use the chat template intended for the selected model; an incorrect template can interfere with how instructions and source text are interpreted. Check the model’s documentation for its expected template and configuration. Also confirm that the source passage fits in the context window and that the output limit is large enough to return the full edit. The LocalAI configuration documentation describes configurable context size and max_tokens behavior.
Test settings on representative writing
A small, controlled comparison is more useful than changing several parameters at once. Use text similar to what you actually edit, including its typical mistakes and stylistic choices.
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- Choose a short representative passage.
- Record the model and version, runtime and version, chat template, prompt, and initial parameter values.
- Generate a baseline using those settings.
- Change one control, such as temperature, and repeat. If randomness is nonzero, compare more than one run.
- Check for missed errors, introduced errors, changes in meaning or voice, damaged formatting, added material, and incomplete output.
- Keep a change only if it consistently helps on your model and text type. Repeat the check after model or runtime updates because defaults may change.
Use the same model, template, passage, and prompt for each comparison. Runtime defaults tell you where a configuration starts; they do not establish which settings produce the best copyedits.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the available evidence can—and cannot—show
The llama.cpp server documentation describes generation controls including temperature, top_p, top_k, repetition penalties, output length, prompt retention, grammar-constrained sampling, and seed, and gives defaults for several controls. LocalAI’s model configuration documentation lists common parameter defaults and ranges and explains context and model configuration. These materials explain mechanics; they do not benchmark proofreading quality.
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A 2024 paper, Optimizing Large Language Model Hyperparameters for Code Generation, evaluates GPT-3.5 Turbo on 13 Python tasks and analyzes 14,742 generated code segments. Its findings concern code generation, not copyediting or local inference, so its parameter results should not be treated as proven grammar-editing settings. Likewise, the community-maintained text-generation-webui guide offers broad assistant-chat guidance, not a copyediting study.
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