uma-cognitive-llm-checkpoints

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. Directories under */instruct_* load on Qwen/Qwen3-4B-Instruct-2507; everything else loads 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

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). Seed-variance runs (seeds 43-46) remain on the cluster; ask if needed.