Instructions to use ceselder/maemm-uplift-acts100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ceselder/maemm-uplift-acts100 with PEFT:
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- Notebooks
- Google Colab
- Kaggle
MAEMM cross-uplift arm acts100: 200k real activations only (control)
Midtrain (1 epoch, lr 1e-4) on a 200k bank of 200k real activations only (control), from the 23M real-activation SFT init, then 100 RL steps (CISPO / ScaleRL, 128 directions × 16 samples per step, lr 1e-5, 10 warmup steps) on the same bank. Report: http://5.78.192.0/reports/view/maemm-uplift-matrix/report.html
All checkpoints are 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; text whose clean layer-42 activation points along v). Code: https://github.com/ceselder/maemm. Eval = 512 held-out directions/family, best-of-4 at T=1, cosine of the clean base L42 activation (max over last 5 tokens). 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) | 0.252 | 0.310 | 0.222 | 0.092 | 0.238 | 0.183 | 0.052 |
| rl_step_25 | 0.333 | 0.424 | 0.324 | 0.143 | 0.277 | 0.213 | 0.105 |
| rl_step_50 | 0.362 | 0.468 | 0.395 | 0.189 | 0.290 | 0.226 | 0.128 |
| rl_step_100 | 0.377 | 0.493 | 0.427 | 0.199 | 0.300 | 0.230 | 0.150 |
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Model tree for ceselder/maemm-uplift-acts100
Base model
Qwen/Qwen3.6-27B