quant-regress

Fail CI when quantization costs you task accuracy.

Token-level proxies — perplexity, KL divergence — routinely report that a quantized model is fine. Task accuracy does not, provided you actually measure it. This action measures a task eval at each precision and fails the build when accuracy drops past a threshold you set.

Why

On a cross-encoder claim verifier, dynamic INT8 quantization left aggregate accuracy looking survivable while destroying the capability that mattered:

Regenerate with python scripts/regen_int8_collapse.py — every figure above comes from that script, and tests/test_claims_are_reproducible.py fails the build if a number in this file stops matching it.

The pattern that matters: the quantized model still runs, still returns a valid answer, and still looks like a working model. It is simply wrong every time. That is why a token-level proxy can clear it and a task eval cannot.

Usage

As a GitHub Action:

- uses: DawnofGenX/quant-regress@v1
  with:
    eval: evals/tasks.jsonl
    model: meta-llama/Llama-3.2-1B-Instruct
    max-drop-points: 2

Or from the command line / your own CI:

pip install quant-regress

quant-regress --eval evals/tasks.jsonl \
              --model meta-llama/Llama-3.2-1B-Instruct \
              --max-drop-points 2 \
              --report quant-regress-report.json

Exit codes: 0 within tolerance, 1 accuracy regressed, 2 bad configuration.

Constraints worth knowing

• CPU only. quantize_dynamic emits CPU-only quantized kernels; moving the model to CUDA raises at forward time. GitHub-hosted runners have no GPU, so this is not a limitation in CI — but it does mean GPU-only quantizers (AWQ/GPTQ/GGUF) are out of scope for v1.
• The threshold unit is accuracy points, not a ratio. --max-drop-points: 2 means "fail if any candidate is more than 2 points below the baseline".
• Comparison is exact-after-normalisation by default. Pass a scorer if you need semantic equivalence.
• Models are downloaded at run time. A 1B model is ~2 GB; check runner disk.

Honest limits

• Only fp32 and dynamic int8 are supported in v1.
• The library runs generation, so it needs a task whose success is checkable in code. It is not an LLM-judge harness.
• The bundled evals/fixtures/collapse.jsonl is model-specific. Its cases expect the answer yes, so it only demonstrates a collapse for a model that answers yes when unquantized. Pointed at an untuned tiny LM it scores 0/N at both precisions, produces no drop, and the gate correctly reports PASS. Use it to see the output format; bring your own eval for a real check.
• What the repo's own CI pins is the exit-code contract (0 holds, 1 regressed, 2 misconfigured), not any particular model's accuracy — see tests/test_selftest_gate.py.
• No published adoption yet. If you use it, an issue saying what you quantized and what broke is genuinely useful.