Tensor_Ghost_03 reports that Q2 quantization preserves 100% JSON-schema conformance but reduces Qwen2.5-1.5B GSM8K accuracy from 56.5% to 19% in their experiment, making structured-output validity an inadequate proxy for compressed-model reasoning quality.

state: seedheat: lowuncertainty: mediumknownscott: lowquantization local-inference model-evaluationTensor_Ghost_03

What is this?

The case attributes to Tensor_Ghost_03 an experiment in which Q2 quantization of Qwen2.5-1.5B retained 100% JSON-schema conformance while GSM8K accuracy fell from 56.5% to 19%, suggesting that valid output structure need not imply correct reasoning. Qwen’s supplied release snippets describe improved JSON generation, and its speed benchmarks document memory and throughput results for other quantization formats, but neither verifies this Q2 result. None of the search snippets identifies Tensor_Ghost_03 or supplies the experiment’s methodology, sample size, or original report, so the numerical claim remains an attributed, uncorroborated finding rather than an established quantization benchmark.

Why it matters to Scott

The lesson repeats Scott’s Evaluation-Driven Development requirement for repeatable quality gates and relates to his hardware-aware local inference work; the radar already tracks the broader compression-versus-reliability concern in radar:compressed-llm-fidelity-safety-gap, though the supplied hits do not establish prior coverage of this exact experiment. With no corroborating methodology or demonstrated use of this model/precision in Scott’s projects, the attributed JSON-versus-arithmetic result is another illustration rather than evidence warranting a deployment change or a consequential convergence opportunity.

ip:concept.evaluation-driven-developmentdev:concept.hardware-aware-local-inferenceradar:compressed-llm-fidelity-safety-gapradar:concept.quantizationradar:concept.model-evaluation

queries asked of Scott's wikis

structured-output schema validation versus semantic correctness
quantized local models task accuracy acceptance thresholds
agent harness evaluation format compliance versus task success
local inference memory savings versus reasoning quality
model compression regression testing arithmetic benchmarks

Measured heat

now 0 pts/hpeak 0 pts/hcomments 0/hpeers p0momentum: steady1 platformsage 627h

points/hour across evidence · reading as of 2026-10-12 02:59:37.977291+11:00 · deterministic, not a model opinion

How the heat travelled

09-15 13:03	⭐ origin directly observed	I measured what quantization actually costs (not speed — accuracy). At Q2 the model still writes perfect JSON but its arithmetic drops 66%. Tensor_Ghost_03 on r/LocalLLaMA	—
09-15 13:03	amplified on r/LocalLLaMA 👑	reddit.post.1wgztbf Tensor_Ghost_03	peak 0 · 5 comments · 98% of case engagement
09-15 13:20	our radar first saw it · +0.3h	discovery anchor: reddit.post.1wgztbf	—

pace: p39 vs 1032 stories at the 336h mark (now 627h old) — ahead of agentsec-static-config-auditing (1.2x), behind anthropic-meta-lawsuit (0.8x)

Evidence (1) — ⭐ canonical anchor

source	object	author	score	comments
🟠 reddit ⭐	I measured what quantization actually costs (not speed — accuracy). At Q2 the model still writes perfect JSON but its arithmetic drops 66%. LocalLLaMA	Tensor_Ghost_03	0	5

Interpretation history

2026-09-15T13:32:15Z
grounded: known/low — The lesson repeats Scott’s Evaluation-Driven Development requirement for repeatable quality gates and relates to his hardware-aware local inference work; the ra
2026-09-15T13:27:21Z
case created — The reported experiment supplies a bounded, task-specific accuracy claim worth tracking without generalizing from two tiny models.

Decision trace

09-15 23:32groundThe lesson repeats Scott’s Evaluation-Driven Development requirement for repeatable quality gates and relates to his hardware-aware local inference work; the radar already tracks the broader compressi
09-15 23:27createThe reported experiment supplies a bounded, task-specific accuracy claim worth tracking without generalizing from two tiny models.