--- license: apache-2.0 tags: - benchmark - quantization - gptq - awq --- # QuantBench leaderboard data Raw benchmark data behind the [QuantBench leaderboard](https://quantbench.pages.dev): calibration-quality GPTQ/AWQ quantization results across model sizes, calibration corpora, and GPU tiers. **331 rows** (239 ok / 92 failed — failed runs are published too; a documented failure is a finding, not noise). ## Models - `Qwen/Qwen2.5-1.5B-Instruct` (1.5B) - `HuggingFaceTB/SmolLM2-1.7B-Instruct` (1.7B) - `deepgrove/Bonsai` (0.5B) - `Qwen/Qwen2.5-3B-Instruct` (3B) — licence pending, rows only, no weights here - `HuggingFaceTB/SmolLM3-3B` (3B) ## Files - `rows.csv` — the full table (see the leaderboard site for column definitions and the methodology page for the eval protocol). - `.json` — one file per row, same fields as its `rows.csv` line. - `niche_rows.csv` — task-level niche columns over the same artifacts (HumanEval[0:50] pass@1, FiQA-2018 sentiment accuracy [flare-finqa is gated for this token — instrument recorded per row], SEC-filing context drift, 10-prompt ROUGE-L consistency), each vs a same-stack fp16 baseline; failures/skips recorded as rows. - `logs/` — the driver log, last heartbeat, and spend ledger from the run that produced this data (transparency, not polish — these are raw operational logs). - `pools/BUILD.md` — exact recipe to reproduce the calibration/eval text pools. **No third-party corpus text is redistributed here.** ## Honesty notes carried into every row - GPTQ rows in this sweep ran gptqmodel's torch-fallback kernel path (`load_backend` column), not the optimized kernel — perplexity is unaffected, throughput numbers understate what a working kernel would show. - fp16 baselines are per-stack (autoawq vs gptqmodel use different torch/transformers pins), so `ppl_delta_vs_fp16` is only comparable within one stack and one GPU tier, never across them. - `webgpu_runnable` is `[UNVERIFIED]` on every row — no browser measurement was run. - Machine-generated benchmark output. Independent verification welcome; treat any single row as a data point, not a certified result.