Broken on Arrival: Non-Functional Quantized LLM Artifacts in Public Model Registries and How to Catch Them
Abstract
Quantized small language model files in public registries can be non-functional due to uncalibrated quantization recipes, and screening plus calibration is recommended before release.
Developers download quantized large language models (LLMs) from public registries to run them locally. None of the registries we examined documented functional testing of their files. We tested all 305 eligible code-capable GGUF tags (the llama.cpp format) across 15 model lines in the official Ollama library, plus 22 popular Hugging Face community files. A 15-task code screen identified suspects, which we retested on all 164 HumanEval+ tasks. Seven official tags (2.3%) solved at most six of them. All were low-bit quantizations of models with at most four billion parameters. We rebuilt five suspect files from the original weights. All five registry files matched our uncalibrated rebuilds tensor for tensor, so none was corrupted. Exact reproducibility did not imply function. With the weights, toolchain and tensor mix held fixed, adding an importance matrix raised three Qwen2.5-Coder-3B builds from zero to 76-113 solved tasks. The same uncalibrated recipe left the smaller Qwen2.5-Coder-1.5B functional, showing that the recipe alone does not predict failure. None of 66 sampled tags that passed the screen was non-functional. We release the audit data and the quantcheck tool and recommend screening low-bit tags before publication, then calibrating or withholding failures.
Community
We ran every eligible code-capable GGUF tag in the official Ollama library (305 tags across 15 model lines) plus 22 popular Hugging Face community files through a 15-task code screen, then retested suspects on all 164 HumanEval+ tasks. Seven official tags solve at most six tasks. All are low-bit quantizations of models with at most 4B parameters, and the five we rebuilt match the registry files tensor for tensor, so the files are not corrupted: the uncalibrated recipe is the cause. Adding an importance matrix lifts Qwen2.5-Coder-3B from 0 to 76 to 113 of 164 tasks at three quantization levels. The audit data is on the Hub as aditiPatodiya/quantcheck-audit; the screening tool and rebuild scripts are in the linked repository.
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I've had this bite me with a 4-bit of a 7B I grabbed for an edge box — it answered chat fine, then every tool call came back as near-JSON with a stray comment in it, so my parser threw and the agent looped. Nothing in the card hinted at it. The failure mode wasn't "can't code," it was "can't hold a schema," and no benchmark on that repo would have told me.
So the clustering you found matches what I'd expect, but a code screen is the wrong tripwire. Small quants lose structured output and mid-context recall well before they lose HumanEval ability, and those two things are what actually break a deployment. What I want from a registry is ugly and boring: a fixed prompt suite — JSON schema conformance, a needle at 50% context, a couple of tool-call round trips — scored per file at upload and shown as numbers, not a "quantized" badge. Even a pass/fail per prompt would save people a weekend.
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