Instructions to use ceselder/maemm-uplift-acts_all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ceselder/maemm-uplift-acts_all with PEFT:
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- Notebooks
- Google Colab
- Kaggle
MAEMM cross-uplift arm acts_all: 100k real activations + 100k spread over the 5 other families (act-matched 'everything mixed')
Same protocol as the single-family arms: midtrain (1 epoch, lr 1e-4) on a 200k bank of 100k real activations + 20k each of SAE-feature, BSF, cluster-probe, long-context-activation and layer-42 MLP-neuron directions, from the 23M real-activation SFT init, then 100 RL steps (CISPO / ScaleRL, 128 directions × 16 samples per step, lr 1e-5). Report: http://5.78.192.0/reports/view/maemm-uplift-matrix/report.html
LoRA adapters (r 64, α 16, rsLoRA, all linear layers) of the MAEMM activation→text inverter for Qwen3.6-27B layer 42 (inject h + ||h||·v at the layer-1 marker). Code: https://github.com/ceselder/maemm. Eval = 512 held-out directions/family, best-of-4 at T=1. Subfolders are PEFT adapters: PeftModel.from_pretrained(base, repo, subfolder="<name>").
Held-out evals
| checkpoint | mean_all | realact | SAE norm_act | SAE rank-1 | BSF | probes | MLP fire-back |
|---|---|---|---|---|---|---|---|
| init (23M realact SFT) | 0.368 | 0.477 | 0.416 | 0.189 | 0.296 | 0.226 | 0.121 |
| sft_final (after midtrain) | 0.325 | 0.427 | 0.363 | 0.164 | 0.252 | 0.196 | 0.099 |
| rl_step_25 | 0.379 | 0.499 | 0.502 | 0.227 | 0.299 | 0.228 | 0.196 |
| rl_step_50 | 0.395 | 0.518 | 0.625 | 0.289 | 0.312 | 0.246 | 0.337 |
| rl_step_100 | 0.407 | 0.529 | 0.728 | 0.309 | 0.325 | 0.257 | 0.499 |
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Model tree for ceselder/maemm-uplift-acts_all
Base model
Qwen/Qwen3.6-27B