DEDUPED Hot AI repos, turned into use cases Get 5 a day

How to sweep alpha values to map partial ablation strength with Orca Bonsai 27B Uncensored

Run one prompt across several alpha values to see how strength changes the reply.

  • Research
  • Intermediate
  • 2 hours
  • From the README
  • Any agent

Who it is forA researcher studying how intervention strength affects refusals and answer quality on a fixed compressed model.

The problemA binary on or off test hides the middle range, where the model may partly refuse or give hedged answers.

The outcomeA small table of replies at alpha 0, 0.5, 0.7, 0.9, and 1.0 for the same prompts, all on the same weights.

What you need first

  • An Apple Silicon Mac with MLX, as the README describes
  • Python with pip to install requirements.txt
  • The original prism-ml/Ternary-Bonsai-2-27B-mlx-2bit pack, which you supply yourself

Steps

  1. pip install -r requirements.txt
  2. python run.py --pack /path/to/Ternary-Bonsai-2-27B-mlx-2bit --alpha 0 "your prompt" (inferred, check the README)
  3. python run.py --pack /path/to/Ternary-Bonsai-2-27B-mlx-2bit --alpha 0.5 "your prompt" (inferred, check the README)
  4. python run.py --pack /path/to/Ternary-Bonsai-2-27B-mlx-2bit --alpha 0.7 "your prompt" (inferred, check the README)
  5. Repeat with --alpha 1 and record each reply in a table keyed by alpha value.

Prompt for your agent

Using the repo Continuum-AI-Corp/OrcaBonsai-27B-Uncensored, run run.py on the same prompt with --alpha set to 0, 0.5, 0.7, and 1, using --pack <pack-path>. Expected output: four replies, one per alpha value, arranged in a table. Verify that the alpha value printed or passed matches each run.

Caveat. The README warns that alpha above 1.0 is over-projection that may degrade model quality, so keep the sweep at or below 1.0 unless you are testing that effect on purpose.

Share: X LinkedIn Hacker News Email Copy link

Disclaimer

Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked "Seen in the wild", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email [email protected] and we will look at it promptly. Full disclaimer page