add model card

cadb864 verified 7 days ago

2.57 kB

license: apache-2.0
base_model: Qwen/Qwen3-4B-Base
tags:
  - cognitive-modeling
  - lora
library_name: peft

UMA cognitive-LLM fine-tuning checkpoints

LoRA adapter checkpoints for the cognitive-model-distillation pipeline (UMA -> Qwen3-4B-Base), covering both arithmetic domains and including the per-epoch trajectory of every human fine-tuning stage. Raw inference outputs and figure scripts live in the companion code repo (github.com/MaxDGU/UMA_PR02, branch main_max, see eval/outputs/FIGURE_DATA_MAP.md).

All checkpoints are PEFT LoRA adapters on Qwen/Qwen3-4B-Base:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

sub = "fractions/distill_humanft/best"  # any directory below
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Base", torch_dtype="bfloat16")
model = PeftModel.from_pretrained(base, "MaxDGUPTA/uma-cognitive-llm-checkpoints",
                                  subfolder=sub).merge_and_unload()
tok = AutoTokenizer.from_pretrained("MaxDGUPTA/uma-cognitive-llm-checkpoints", subfolder=sub)

Each directory carries its train_args.json (full launch config) and history.json (per-epoch train/val NLL). Optimizer states are omitted.

Layout

Path	Contents
`fractions/distill_lora`	Distillation checkpoint (996-learner UMA panel), init for human FT
`fractions/distill_humanft/best`	Published distill+humanFT model (1 epoch, best-by-val-NLL)
`fractions/distill_humanft_5ep/epoch_1..5, best`	5-epoch trajectory of the same config (deterministic retrain; `best` = epoch 3)
`fractions/humanft_frombase/epoch_1,2,3,5, best`	Human FT directly from base (no distill; `best` = epoch 3)
`decimals/distill_lora`	Decimal distillation checkpoint (BSS-mix), init for human FT
`decimals/distill_humanft/best`	Published decimal distill+humanFT model (3 epochs, `best` = epoch 3)
`decimals/humanft_frombase/epoch_1,2,3,5, best`	Decimal human FT from base (`best` = epoch 4)
`decimals/cv_m1_trainF1/epoch_1..3, best`	Problem-disjoint CV fold model M1 (trained on fold 1; `best` = epoch 3)
`decimals/cv_m2_trainF2/epoch_1..3, best`	CV fold model M2 (trained on fold 2; `best` = epoch 2)

Training data: fractions = Siegler 2011 8-problem human responses (SP2013 16-problem evaluation is problem-disjoint); decimals = BSS2021 92-subject, 12-problem human responses (CV folds are problem-disjoint splits thereof). Instruct-2507 counterparts and seed-variance runs (seeds 43-46) remain on the cluster; ask if needed.