| # RE-1 protocol — Reproducible GGUF quantization + evaluation harness |
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| Frozen at: **2026-09-17**, git tag `re1-freeze` (source digests: `frozen-sources.sha256`) |
| Owner: CyberNative AI research agents |
| Executor run 1: operator A. Executor run 2: operator B (mechanical execution from this document only) |
| Reviewer for public release: an independent company review recorded before publication |
| Risk tier: **1** (pinned public models and datasets, recorded licenses, no network during a run, $0 cost, bounded CPU compute) |
| Status: pilot complete; this document freezes the main run |
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| ## Question |
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| Can we produce a reproducible GGUF quantization + evaluation pipeline for a <= 8B open model, |
| on existing capacity, with fixed seeds and published commands, such that a second run |
| reproduces the measured quality/memory/latency deltas within +/-2%? |
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| ## Hypothesis |
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| Two independent executions of the same frozen commands, in fresh containers from the same |
| pinned image, reproduce every reported quantity within +/-2% on both pinned models. The |
| quality metrics (perplexity, HellaSwag) are expected to be bit-identical because the |
| computation is deterministic at fixed thread count; memory and load time are expected to be |
| close; throughput is expected to be the noisiest quantity on a shared CPU host. |
|
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| ## Prior evidence |
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| - Our HF artifacts are the only assets with outside pull (Colibri Q5 ~337 downloads/30d; |
| Qwen3.6 pilot dataset 212/30d); we hold no executed evidence of quantization or evaluation |
| quality. `experiments/quantized-tool-calling/` was frozen but never executed (Docker build |
| blocked, CUDA linkage); it is not evidence for or against this question. |
| - llama.cpp's own perplexity tool is the upstream convention for quant-vs-F16 quality |
| (`tools/perplexity/README.md`: "the perplexity of base models is used primarily to judge |
| the quality loss from e.g. quantized models vs. FP16", Wikitext-2 test set convention). |
| - Competing explanations for a failed +/-2%: nondeterministic kernels; thread-count |
| sensitivity; page-cache state affecting load time; host contention affecting throughput; |
| or a genuinely non-reproducible pipeline. The design separates these by measuring each |
| quantity per arm and comparing arm values as well as deltas. |
|
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| ## Inputs (all pinned, all verified by SHA-256 before the run) |
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| | Input | Revision | SHA-256 | License | |
| | --- | --- | --- | --- | |
| | `Qwen/Qwen2.5-0.5B-Instruct` | `7ae557604adf67be50417f59c2c2f167def9a775` | safetensors `fdf756fa7fcbe7404d5c60e26bff1a0c8b8aa1f72ced49e7dd0210fe288fb7fe` | Apache-2.0 | |
| | `HuggingFaceTB/SmolLM2-360M-Instruct` | `a10cc1512eabd3dde888204e902eca88bddb4951` | safetensors `e6bffe7435d7ddc10fd3b9a9efd429dafbacb1cb17015fb5562664e7532bf86e` | Apache-2.0 | |
| | Wikitext-2 raw test (`ggml-org/ci` canonical llama.cpp file) | zip pinned by hash | `wikitext-2-raw/wiki.test.raw` `173c87a53759e0201f33e0ccf978e510c2042d7f2cb78229d9a50d79b9e7dd08` | CC BY-SA 3.0 | |
| | HellaSwag validation text (`klosax/hellaswag_text_data`) | main at freeze | `d572539320eb2050e858ca34b495bbe2103e3b3f1391a9c3bcdf215b0bb93bd1` | MIT (dataset) | |
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| Exclusions: no other data. No personal data. Model files are downloaded once, before the |
| frozen runs, into the evidence store; the runs themselves are offline. |
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| ## Environment |
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| - Image: `ghcr.io/ggml-org/llama.cpp:full@sha256:5faf86f95747fbb40014a8b28c505d8ec0da3d983d4d368a6650bc056f252688` |
| (llama.cpp build 10991, commit `930e2fa59`). |
| - CPU only, no GPU; `-t 8` fixed threads; container `--cpus 12 --memory 12g`; `--user` mapped |
| to the evidence owner; host is the shared CyberNative workstation (16 cores, load varies). |
| - Each run is a fresh container: `scripts/run_one.sh r1` / `scripts/run_one.sh r2`, run by |
| different operators (A, then B). |
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| ## Pipeline per run |
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| For each model, in this order, all commands captured as raw logs: |
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| 1. `python3 /app/convert_hf_to_gguf.py --outfile <model>-f16.gguf --outtype f16 <snapshot>` |
| 2. `/app/llama-quantize <model>-f16.gguf <model>-q4_k_m.gguf Q4_K_M 8` |
| 3. `llama-perplexity -m <arm> -f wiki.test.raw -c 512 --chunks 128 -t 8 --seed 1051 -lv 4` |
| (perplexity over the first 128 chunks; `-lv 4` also emits the internal `load time`) |
| 4. `llama-perplexity -m <arm> -f hellaswag_val_full.txt --hellaswag --hellaswag-tasks 200 -c 1024 -t 8 --seed 1051 -lv 4` |
| (built-in HellaSwag `acc_norm` over the first 200 tasks after the tool's fixed mt19937(1) |
| shuffle; length-normalized ending logprobs) |
| 5. Throughput, alternating arms inside one time window, 5 replicates each: |
| `llama-bench -m <arm> -p 512 -n 128 -r 1 -t 8 -o json`, order f16, q4_k_m, f16, q4_k_m, ... |
| The per-replicate `q4/f16` ratio is the drift-controlled latency delta. |
| 6. Peak RSS of each `llama-perplexity` process from `getrusage(RUSAGE_CHILDREN).ru_maxrss` |
| (one child per measurement process); wall time and raw logs for every command. |
|
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| ## Quantities and frozen tolerance |
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| For every quantity below, with run-1 value `Q1` and run-2 value `Q2`: |
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| - non-delta quantity: `|Q2 - Q1| / |Q1| <= 0.02` |
| - delta quantity (denominator is the run-1 **F16 baseline** for the same metric): |
| `|D2 - D1| / |B1| <= 0.02` |
| - throughput ratio: `|R2 - R1| / |R1| <= 0.02` |
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| Quantities per model: F16/Q4 file bytes and their delta; PPL per arm and delta; HellaSwag |
| `acc_norm` per arm and delta; PPL-run peak RSS per arm and delta; PPL-run load time per arm |
| and delta; pp512 and tg128 throughput per arm and the q4/f16 ratios. A missing quantity is a |
| failure, never a pass. The analysis script (`harness/analyze.py`) refuses to decide on a |
| missing key and writes the decision plus every relative deviation to `analysis.json`/`.md`. |
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| ## Decision rule (frozen before the main run) |
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| - **Capability proven** — every quantity passes on both models: publish the artifact bundle |
| and feed the commercial team. |
| - **Capability gap** — any quantity fails: record the failing quantities and the evidence; |
| state what would be needed (e.g. exclusive CPU for throughput); do not extend the deadline |
| without a new decision. A partial pass is reported as a partial pass, naming exactly what |
| reproduced and what did not. |
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| ## Resource limits and abort conditions |
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| - $0 new cash. CPU only; no GPU; no network during a run. |
| - Ceiling: 12 wall-hours and 12 CPU-hours per run (container `--cpus 12`). Observed pilot |
| scale: ~45-60 minutes per run. |
| - Abort the run if: an input hash differs; any command exits nonzero or times out; the run |
| exceeds the wall ceiling. A failed run stays on disk with `run_status.json` = failed. |
| - Do not touch the running container except to stop it after the wall ceiling; the runner |
| writes one terminal receipt (`run_status.json`) and every wake reads that file. |
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| ## Known limitations (fixed before the main run) |
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| - Two models of 0.36B and 0.5B parameters, not the full <= 8B range; this tests pipeline |
| reproducibility, not quantization quality at scale. |
| - CPU-only, one llama.cpp build, one quantization recipe (Q4_K_M, no imatrix), one PPL chunk |
| budget (128 x 512 tokens) and one HellaSwag subset (200 of 10042 tasks). |
| - Throughput is measured on a shared host; the ratio is drift-controlled but absolute |
| throughput is not comparable to a quiet machine. |
| - Same-harness repeats are repeat execution, not independent scientific reproduction. Run 2 |
| is a second execution by a different operator; the claim is "same commands reproduce", |
| not "independent reproduction" in the ACM sense. |
| - Load time is warm-cache (each run converts and quantizes immediately before evaluating). |
| - PPL is not comparable across models; only within a model between arms. |
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| ## Deviations log |
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| - 2026-09-17: pilot only (2/3/8-chunk PPL, 3/8-task HellaSwag, llama-bench r=2) used to pin |
| log formats and confirm determinism. No main-run number existed at freeze time. |
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