System One (phase2 student)
Local open-weight decision model: JSON or text state in, typed choice / score / noul out. One forward pass, no text generation.
This Hub repo is the ship checkpoint from mateolafalce/system-one-model: ModernBERT-base + LoRA r=16 + a 2-layer fp32 scoring head. Load it with that repo's StudentModel.from_pretrained.
Files
| Path | What |
|---|---|
backbone/ |
PEFT LoRA adapter on answerdotai/ModernBERT-base |
head.pt |
Decision head (fp32) |
student.json |
Backbone name, head depth, val metrics |
tokenizer/ |
Tokenizer snapshot |
temps.json |
Temperature scaling (noul K=2 T=0.6; score K=3–5 T=0.7) |
Test numbers (proof gold)
| Task | Acc | ECE |
|---|---|---|
| BANKING77 (77-way) | 88.5% | 0.059 |
| BANKING77 coarse (8-way) | 95.4% | 0.016 |
| SMS spam | 98.9% | 0.009 |
| SST-5 | 55.9% | 0.033 |
BANKING77 missed the 90% ship gate by 1.5 pt. The frozen teacher (Qwen2.5-7B-Instruct-AWQ, letter-logit scoring) ceilings at 56% on that task. Do not scale the student to chase 90%.
Serve
From the GitHub repo, with this folder as --ckpt:
python scripts/07_serve.py --ckpt . --temperatures temps.json --port 8010
POST /v1/systemone with a state and typed questions. Cap is 512 tokens. English only.
Hardware
Fits an 8 GB RTX 3070 (~0.6 GB VRAM at serve). Do not load the Qwen teacher on the same GPU at the same time.
Model tree for lafalce/system-one-model
Base model
answerdotai/ModernBERT-base