by Extraltodeus · LLM Plugin · ★ 203
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J-Wash Reshape a model's identity and behavior by editing token directions, then bake those edits into a real checkpoint you can run anywhere. No training, no dataset, no fine-tuning. J-Wash is a local studio (FastAPI + React) for exploring and editing the J-space of any Hugging Face decoder LLM. You chat with a model while a live Jacobian lens shows what each layer is "reading," pin and inspect concepts, then wash the model's identity or behavior with a few token-level rules — turn "I am a large language model" into "I am a large language fish" — and export the result as a standalone model (full checkpoint, modified layers, or LoRA): standard safetensors weights that load anywhere models do. The editing preview runs live in the chat, and the exported checkpoint reproduces it faithfully — the whole point of the project is that what you see is what you ship. Introducing non-expert friendly alignment! What it's built on J-Wash is built on Anthropic's Jacobian lens (the library), a method that reads each layer's contribution to the residual stream through the model's own un-embedding.
| Stars | 203 |
| Forks | 20 |
| Language | Python |
| Category | LLM Plugin |
| License | Apache-2.0 |
| Quality Score | 69.4425312827607/100 |
| Open Issues | 3 |
| Last Updated | 2026-08-01 |
| Created | 2026-07-13 |
| Platforms | python |
| Est. Tokens | ~16k |
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J-Wash is Jacobian-Brainwash : A manual alignment tool for large language models built on Anthropic's Jacobian Lens. Results are exportable.. It is categorized as a LLM Plugin with 203 GitHub stars.
J-Wash is primarily written in Python. It covers topics such as abliteration, ai, anthropic.
You can find installation instructions and usage details in the J-Wash GitHub repository at github.com/Extraltodeus/J-Wash. The project has 203 stars and 20 forks, indicating an active community.
J-Wash is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to J-Wash on Agent Skills Hub include quanta-quest, context-compressor, aimirror. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.