Instructions to use ceselder/maemm-uplift-acts_cluster with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ceselder/maemm-uplift-acts_cluster with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
MAEMM cross-uplift arm acts_cluster: 100k real activations + 100k cluster-probe directions
Midtrain (1 epoch, lr 1e-4) on a 200k bank of 100k real activations + 100k cluster-probe directions, 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) | 0.282 | 0.349 | 0.456 | 0.193 | 0.257 | 0.210 | 0.246 |
| rl_step_25 | 0.350 | 0.446 | 0.598 | 0.275 | 0.294 | 0.238 | 0.373 |
| rl_step_50 | 0.384 | 0.497 | 0.688 | 0.330 | 0.311 | 0.256 | 0.433 |
| rl_step_100 | 0.403 | 0.526 | 0.730 | 0.320 | 0.324 | 0.266 | 0.501 |
- Downloads last month
- -
Model tree for ceselder/maemm-uplift-acts_cluster
Base model
Qwen/Qwen3.6-27B