README.md

metadata

tags:
  - lora
  - peft
  - quantization
  - glue
  - model-merging

GLUE LoRA x Backbone Bit-Width — combined results

Aggregated metrics, LoRA geometry analysis and figures for a controlled study of whether backbone bit-width (bf16 / int8 / nf4) changes what a LoRA adapter learns.

Grid: 2 model sizes (1B, 3B) x 4 GLUE tasks (MNLI, QQP, SST-2, RTE) x 3 bit-widths x 3 seeds = 72 runs (0 present here).

For each (model size, seed) the adapter initialisation is identical across bit-widths, so cross-bit differences are attributable to the backbone. The headline statistic is

Gamma = mean cross-bit-width adapter distance / mean within-bit-width seed distance

Gamma > 1 means bit-width perturbs the adapter more than the random seed does.

Files

• results_summary.md — the report, answering the 8 study questions
• aggregate_results.csv — per-run metrics
• aggregate_results_by_task.csv, aggregate_results_by_condition.csv
• adapter_layer_metrics.csv — per-adapter per-layer geometry (norms, effective rank, stable rank, singular values, energy)
• pairwise_geometry_metrics.csv — per-pair per-layer comparisons (Frobenius distance, cosine, principal angles, projection overlap, sign agreement)
• gamma_table.csv — bit-width vs seed effect sizes
• plots/ — 7 figures

Adapters live in Jeesup/llama32-<size>-<task>-<bitwidth>-lora-seed<seed>.