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Every method starts from the same base weights and trains on the same task rows for the same steps.","modelSha256":"67600098679443bf87c78f0f3ef1a1399ae51642b3e9608517105d7831219d5e","modelFile":"media/school/attention/model.json","recipe":{"steps":1500,"seeds":[1,2,3],"batch":16,"lrs":[0.001,0.003,0.01,0.03],"alpha":8,"weightDecay":0,"taskRows":256,"origRows":256,"checkpoints":[0,50,100,250,500,750,1000,1500],"lora":"W_eff = W + (alpha / r) A B, A [inputs, r] uniform in +-1/sqrt(inputs), B [r, outputs] zero"},"task":{"about":"The new task, \"things keep their look\": \"the <thing> was <adjective> .\", then 0 to 2 of the corpus's short guide sentences (tallow lit the lamp; rerun knocked twice; rerun knocked on a shell; pylon watched the dock; pylon stood on the dock), then the thing named again with the same adjective. The corpus's own distractor sentence draws a fresh adjective every time, so the base model has never seen a look kept. Every next word has exact odds by construction; floor is their loss on the task's own 256 held-out rows (6144 targets), idealAccuracy what the exact odds would score (a word with 10 equal choices counts a tenth). answer is the repeated adjective (certain once the thing is named again); repeatThing is the repeated thing. A first design with two things described and one asked about was dropped: no method, full fine-tuning included, learned to tell the two apart in 5,000 steps (all stalled at half right on the answer), so it could not compare methods.","floor":0.5327,"idealAccuracy":0.7572,"rows":256,"exampleRow":"the boat was bright . rerun knocked on a shell . the boat was bright . the boat was wet . rerun knocked on"},"base":{"about":"The base model on the new task before any fine-tuning, and on the original 256 held-out rows of the corpus (origFloor is the corpus generator's own loss there). byRole splits the task score by word role.","task":{"loss":1.6867,"excess":1.154,"accuracy":0.5021,"answer":0.1492,"answerLoss":1.8645,"repeatThing":0.0882},"byRole":{"all":{"n":6144,"accuracy":0.5021,"loss":1.6867,"floor":0.5327,"excess":1.154,"idealAccuracy":0.7572,"lossCheck":1.6867},"thing":{"n":541,"accuracy":0.0388,"loss":4.8215,"floor":2.3026,"excess":2.5189,"idealAccuracy":0.1},"frame":{"n":2388,"accuracy":0.7835,"loss":0.8438,"floor":0,"excess":0.8438,"idealAccuracy":1},"look":{"n":532,"accuracy":0.1767,"loss":1.8636,"floor":1.7918,"excess":0.0718,"idealAccuracy":0.1667},"filler":{"n":1580,"accuracy":0.6411,"loss":0.7506,"floor":0.5121,"excess":0.2385,"idealAccuracy":0.7526},"repeatStart":{"n":378,"accuracy":0,"loss":3.2292,"floor":0.7008,"excess":2.5284,"idealAccuracy":0.5481},"repeatThing":{"n":363,"accuracy":0.0882,"loss":4.5918,"floor":0,"excess":4.5918,"idealAccuracy":1},"answer":{"n":362,"accuracy":0.1492,"loss":1.8645,"floor":0,"excess":1.8645,"idealAccuracy":1}},"orig":{"loss":0.6874,"copyAccuracy":0.9921,"accuracy":0.7243},"origFloor":0.6585},"methods":{"about":"Every method fine-tunes a copy of the base on fresh task rows (data seed 300 + seed) for 1500 steps, batch 16, AdamW with the lab's warmup and clipping and no weight decay (decay would add a full-rank shrink to the change measured in rank). lr was picked per method from 0.001, 0.003, 0.01, 0.03 on seed 1 by the task loss on 256 separate validation rows (validLossByLr); seeds 2 and 3 then ran at that rate. lrAtGridEdge marks a pick at the smallest or largest rate tried, where a better rate may lie outside the grid. Seeds change the data stream and, for LoRA, the random A. trained counts the numbers each method changes (the base has 20864). lora.attn adapts the 4 attention projections, lora.all every dense matrix (attention, feed-forward, readout); both leave the embedding tables, biases and norms frozen. lastLayer trains layer 2's whole block, the final norm and the readout. Scored at the end on the task's held-out rows and the original held-out rows; mean, sd and range over 3 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