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ceselder/maemm-uplift-acts_sae
maemm-uplift-acts_sae is a machine learning model from ceselder. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.
Midtrain (1 epoch, lr 1e-4) on a 200k bank of 100k real activations + 100k SAE-feature directions, from the 23M real-activation SFT init, then 100 RL steps (CISPO / ScaleRL, 128 directions × 16 samples per step, lr 1e…
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Updated Sep 5, 2026
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From the Hugging Face model README
acts_sae: 100k real activations + 100k SAE-feature directionsMidtrain (1 epoch, lr 1e-4) on a 200k bank of 100k real activations + 100k SAE-feature 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>").
| 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) | 0.258 | 0.325 | 0.404 | 0.176 | 0.242 | 0.184 | 0.073 |
| rl_step_25 | 0.339 | 0.440 | 0.576 | 0.244 | 0.281 | 0.218 | 0.157 |
| rl_step_50 | 0.376 | 0.494 | 0.685 | 0.297 | 0.300 | 0.237 | 0.248 |
| rl_step_100 | 0.398 | 0.518 | 0.805 | 0.336 | 0.312 | 0.251 | 0.407 |