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# Automated layer modification and benchmark evaluator

Run systematic layer routing experiments and benchmark model performance across test suites

[Use with BotBento ](https://app.botbento.com/?ref=usecases&utm_source=botbento.com&utm_medium=usecases&utm_campaign=automated-layer-modification-and-benchmark-evaluator)Copy promptShare[Markdown](/text/usecases/automated-layer-modification-and-benchmark-evaluator/index.md)

Opens BotBento. Paste the prompt into a bot; nothing is preconfigured.

- When you askReceive model and layer ranges
- ThenExecute benchmark sweep via terminal
- ResultLog comparative metrics to spreadsheet

Automate model layer routing experiments by configuring layer repetitions, executing standard evaluation harnesses, and compiling benchmark score comparisons across different layer configurations to identify optimal structural modifications.

## Prompt to start with

BotBentoRecommended

```
When given a target model and experimental layer ranges, construct the layer modification configurations. Run the benchmark evaluation suite through the terminal, monitor run health, and parse task scores upon completion. Compile metric tables and append findings to the spreadsheet. Ask for explicit approval before running heavy GPU benchmark jobs, downloading model weights, or deleting past checkpoint files.
```

Copy

## Tools it uses

TerminalSpreadsheetNotes

## Works with

BotBento

## Tags

- machine-learning
- benchmarking
- llm
- evaluations
- automation

## Source

[Show HN: Duplicate 3 layers in a 24B LLM, logical deduction .22→.76. No training](https://news.ycombinator.com/item?id=47431671)
Hacker News · checked 2026-10-15

Summarized in our own words. Any results described there are the authors’ own, not measured by BotBento.

By BotBento Editorial · Added 2026-10-15 · Updated 2026-10-15 · [Suggest a correction](/contact/)

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