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For a restrained first setup, use the model’s correct chat template, try low or zero temperature if your runtime supports it, and prompt the model to fix errors without changing meaning, voice, terminology, or formatting. Leave top_p, top_k, and other sampling controls at their defaults initially. These are starting points to test—not a proven best recipe: available documentation describes generation controls, but does not establish which settings produce the most accurate copyedits.
Why there is no universal best setting
Local language models run through different software, and their defaults and parameter behavior can vary by runtime and model. For example, the llama.cpp server documentation gives a temperature default of 0.8, while LocalAI’s common configuration table lists 0.9. Those are documented defaults, not settings shown to improve proofreading.
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The same distinction applies to general sampling advice. Settings change how text is generated; they are not grammar rules. Your own model, runner, template, prompt, and source passage all affect the result. Treat each adjustment as a hypothesis to check on your material.
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| Setting | Documented behavior or default | Copyediting starting point |
|---|---|---|
| Temperature | Controls randomness. llama.cpp documents a server default of 0.8; LocalAI lists 0.9 in its common parameter table. | Try a low value or zero if the runtime and model accept it and edits are too creative. Check the result; zero does not guarantee correct edits or identical behavior across implementations. |
top_p and top_k |
Restrict the pool of candidate next tokens. llama.cpp documents defaults of 0.95 for top_p and 40 for top_k. |
Leave both at their defaults at first. Avoid sharply restricting them while also changing temperature, so you can tell which adjustment affected the output. |
| Repetition penalty | In llama.cpp, repeat_penalty has a documented default of 1.1 and repeat_last_n a default of 64. |
Begin at the default. If the model actually loops or repeats, adjust gently and inspect the result; strong penalties can affect ordinary repeated words. |
| Frequency and presence penalties | llama.cpp documents disabled defaults of 0.0. LocalAI lists a supported range of -2 to 2. | Keep them neutral unless you observe a specific repetition problem. They are controls related to repetition and diversity, not established grammar correctors. |
| Context and output length | These limits determine how much text can be processed and returned. LocalAI documents configurable context size and max_tokens behavior. |
Allow enough context for the passage and enough output capacity for its complete edited version. Check that the response is not truncated. |
| Seed | llama.cpp documents a seed parameter with a random default of -1. | If your runtime supports it, fix the seed when comparing settings for repeatability. Confirm how that runner handles seeds. |
These defaults and controls are documented in the llama.cpp server README and LocalAI model-configuration guide. Check the documentation for your particular runner and model rather than assuming its labels or defaults match either example.
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Use a prompt that defines the editing boundary
Tell the model both what to fix and what it must preserve. For example:
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.
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This is a practical prompt suggestion, not a tested or source-validated recipe. For higher-impact writing, ask for a separate change list or compare the original and edited versions so you can review substantive changes.
Check the template and text limits before tuning
Use the chat template intended for the selected model; an incorrect template can interfere with how instructions and source text are interpreted. Model-specific documentation is the place to check the intended format and configuration. Also make sure the full passage fits within the context limit and that the output limit leaves room for the complete edit. A response that stops partway through is not a successful copyedit.
General community guidance, such as the text-generation-webui guide, can offer broad assistant-chat suggestions, but it is not a copyediting evaluation and should not override model-specific instructions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Run a small comparison on your own writing
- Choose a representative passage. Include the kinds of errors, terminology, formatting, and stylistic choices that appear in your real work.
- Record the setup. Note the model and version, runtime and version, chat template, prompt, and initial parameter values.
- Generate a baseline. Save the output before changing any settings.
- Change one control at a time. For example, lower temperature while keeping the model, template, passage, and prompt unchanged. If randomness is nonzero, compare multiple runs.
- Compare the edits. Check correction accuracy, meaning and voice preservation, unwanted rewriting or additions, formatting, consistency, and whether the full passage was returned.
- Keep only useful changes. Recheck after model or runtime updates, since documented defaults can change.
There is no directly relevant benchmark here establishing an optimal local-LLM configuration for grammar or copyediting. One neighboring 2024 study, Optimizing Large Language Model Hyperparameters for Code Generation, analyzed 14,742 generated Python code segments using GPT-3.5 Turbo across 13 Python tasks. Its findings concern code generation—not proofreading or local inference—so its parameter results should not be treated as copyediting guidance.
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