Verify a Quantized Model Artifact Before Shipping

Direct answer

A quantized artifact must be reviewed as a specific set of bytes produced by a specific recipe. “INT8,” “4-bit,” or “quantized” describes only part of that recipe; it does not identify calibration data, mixed precision, excluded tensors, tokenizer, converter version, package layout, or supported operations. Record exact source revision, quantization method and flags, toolchain version, calibration provenance if any, output files, length, and trusted digest when one exists. Inspect the package, load it in the target runtime, and run task fixtures. Do not infer smaller download, lower runtime memory, faster inference, or unchanged quality from a label alone. The project fact MEMORY-02 describes a lower-bound weight-storage estimate and warns that real artifacts add metadata or mixed structures. COVE-MODEL-02 states that the current Cove E2B SHA-256 is empty; therefore the current configuration cannot be described as hash-verified. These facts guide what metadata to request, not a claim about a new quantized release. The repository also lacks benchmark results, so performance and accuracy claims need actual raw records. A quantization guide can publish validation method and artifacts schema without presenting made-up values. Keep source model and quantized output distinct. Quantization may transform weight representation, preserve or change operator support, require calibration inputs, and interact with the runtime’s kernels. Check whether the format packages tokenizer and model metadata together or references external files. Make the manifest list all components and bind them to one output revision. If one file changes, re-run package and runtime checks rather than relying on a family-level description. Preserve licenses and source provenance through conversion. Separate storage measurements from runtime measurements. Record exact file length for transfer and installed-storage planning; measure process memory only on the exact runtime and device if such data is needed; measure quality with versioned task fixtures. Parameter count times bit-width is a lower-bound estimate, not actual package bytes or peak memory. Quantization formats can store scales, zero points, metadata, and non-quantized tensors. A quantized model might even be larger than expected because of those additions or companion assets. Avoid universal memory formulas beyond stating their assumptions. Define representative task checks before conversion. Use deterministic or property-based fixtures that verify output structure and required behaviors; record stochastic sampling settings. Compare source and quantized candidates with the same prompt template, tokenizer, runtime path, and task version where that is technically valid. If the pipeline changes those inputs, report the comparison as a different experiment. Do not state equal accuracy because one sample looks similar, and do not turn model-card modality or context into device compatibility. A successful load establishes neither sustained performance nor usefulness. Test failure handling too: missing companion tokenizer, unexpected tensor type, unsupported operator, truncated output file, digest unavailable, package parser failure, and runtime initialization rejection. Quarantine only the failed candidate; keep user data and last accepted model intact. When exact expected hash is unavailable, describe remaining controls—pinned source revision, trusted transport, file length, format parse, load test—without calling that cryptographic integrity verification. A release manifest should be reviewable by someone who did not run the conversion. ### Procedure 1. Pin the source model and quantization recipe: source revision, toolchain commit/version, method, options, calibration data provenance, tokenizer, and target format. 2. Generate a new output revision and inventory every file. Record lengths and a digest only when a trusted expected digest can be established; preserve the source and license references. 3. Inspect metadata and package dependencies. Verify quantized tensor types, supported-op assumptions, tokenizer/config linkage, and external assets with the format’s official tooling. 4. Load the exact package in the intended runtime and run versioned task fixtures. Separate file parse, runtime load, task properties, device measurements, and product acceptance. 5. Review release claims against evidence. Publish no performance or accuracy numbers without raw reproducible records, and retain a known-good artifact plus candidate rejection path. ### Acceptance criteria - The release record identifies source revision, quantization recipe, converter, calibration provenance, output revision, and companion files. - Package inspection and runtime loading pass for the same exact candidate and are recorded as different gates. - Storage, memory, quality, and device claims are not inferred from bit width or model-family metadata. - Missing digest or unsupported operations are disclosed; a failed candidate cannot replace the working artifact. ### Comparison | Label or evidence | Valid interpretation | Invalid shortcut | |---|---|---| | “4-bit” or “INT8” | A quantization format/recipe descriptor | A guarantee of file size, speed, memory, or accuracy | | Exact artifact length | Downloaded or installed bytes for this revision | Runtime peak memory or device compatibility | | Task fixture results | Outcomes for recorded inputs and configuration | Universal quality parity or benchmark results | ### FAQ **Does a 4-bit label guarantee a smaller model file?** No. The package may contain metadata, scales, mixed-precision tensors, and companion files. Inspect the exact artifact inventory and record measured lengths instead of inferring size from the label. **Can quantization be assumed to preserve model quality?** No. Validate the exact output on versioned task fixtures with fixed configuration, and report observed outcomes only. A successful load or one similar answer is not evidence of equal quality. **Can bit width predict phone memory or speed?** No. Runtime memory includes additional buffers and execution state, and speed depends on implementation and hardware. Use raw measurements for the exact artifact and device before making such claims. **What if the package has no trusted digest?** Mark the digest unavailable. Preserve source revision, transport and length evidence, inspect the package, load it, and explain that these controls do not equal cryptographic byte verification.

A team receives a quantized package with only a bit-width label

A supplier hands over a model file marked “4-bit” for a mobile build. The team has no converter recipe, calibration record, output manifest, or digest. It needs to decide what can be tested before the artifact is accepted.

Decision

Treat the file as an unidentified candidate; obtain or recreate provenance, inspect exact package contents, load it, and run a versioned task suite before activation.

Expected outcome

The release record states exactly what was verified and leaves size, performance, memory, and quality unknown where raw evidence is absent.

Evidence to collect

The claim becomes reviewable only when the following evidence is attached to the same artifact and test run:

• Artifact digest and length
• Quantization method source
• Target runtime load result

A runtime comparison is publishable only when artifact identity, build configuration, selected backend, task fixture, and failures are recorded.

Implementation workflow

1. Request the complete recipe. Ask for source model revision, quantization method, converter version and flags, calibration-data provenance, tokenizer revision, target format, and all output files. A bit-width label is not a release manifest.
2. Create a candidate identity. Assign a new revision to the received bytes, record length and digest only from trustworthy sources, and preserve the source/license reference. Do not reuse identity from the unquantized model.
3. Inspect the exact package. Use official tooling to inspect tensor/section metadata, tokenizer and config links, external files, and runtime support assumptions. Record warnings rather than inferring all operations are valid.
4. Run target runtime and task fixtures. Load the same candidate in the intended runtime, then test versioned prompts and acceptance properties. Keep loading, quality evaluation, device measurement, and product acceptance distinct.
5. Approve claims only from records. Use exact file inventory for storage statements and raw device runs for memory or speed. If a digest, recipe, or measurement is absent, state that gap and keep the candidate unapproved.

Keep each transition observable. A failure should identify the stage, artifact, runtime, and recovery action without logging private user content.

Acceptance criteria

• Source, quantization method, tools, calibration provenance, output revision, and companion assets are traceable.
• The exact package passes structure and runtime checks; no result is inferred from its label.
• No size, memory, speed, accuracy, or compatibility claim appears without corresponding raw evidence.
• A missing digest or rejected package is disclosed and cannot replace the active artifact.

Decision table

Failure patterns to prevent

• Using bit width as a quality result
• Comparing different model revisions

Also prevent silent fallback, unpinned artifacts, missing cancellation, and conclusions that combine unlike configurations. Store unsuccessful runs alongside successful ones.

Minimum reproducibility record

Release checklist

• ☐ The primary query is answered without an unsupported number.
• ☐ Every artifact and runtime is pinned.
• ☐ The representative task and failure policy are explicit.
• ☐ Lifecycle, cancellation, cleanup, and fallback are tested.
• ☐ User-content and network boundaries are documented.
• ☐ Result wording applies only to the recorded configuration.
• ☐ The page links to raw method or evidence when results are added.

Sources and related evidence

• Google LiteRT post-training quantization guide
• Google LiteRT converter API reference
• LiteRT-LM model package schema

Chinese deployment and troubleshooting content is organized in the .奇连 AI 端侧专题.

Frequently asked questions