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Quantized Export

Export a promoted fine-tuned model in the right deployment format — merged safetensors, LoRA-only, GGUF with imatrix, or FP8. Use after a checkpoint passes promotion, when choosing a quantization format for a target device, or when an exported model fails its smoke test.

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quantized-export

Quantized Export

The last stop after checkpoint-promotion hands off a PROMOTE verdict: a checkpoint that cleared the four-stage gate still isn't deployed until it's exported in the right format for its target runtime and proven to still work post-export. A REJECT verdict never reaches this skill — export starts only from a promoted checkpoint.

Input: a promoted checkpoint (or LoRA adapter) plus the target deployment surface — GPU class, serving stack, and whether long-context/code/math workloads are in scope. Output format: an exported artifact in the chosen format plus a smoke-test diff report comparing 3–5 golden outputs pre-export and post-export.

Format Map

Pick format by hardware and deployment shape, not by habit — the wrong pick either wastes throughput headroom or breaks silently on specific workloads (see Workload Overrides).

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npx skilletmd add @wshobson/quantized-export -y

Version history

Target	Workload	Format
Datacenter GPU	generic chat	FP8
Datacenter GPU	long-context/code/math	FP8 or W8A8 — never INT4
Older GPU generation	generic	AWQ INT4
Edge device / laptop	llama.cpp serving	GGUF Q4_K_M + imatrix
GB10	any workload	FP8 via vLLM nightly, or GGUF via llama.cpp locally — skip NVFP4

Quantized Export

Quantized Export

Format Map

Worked Picks

Workload Overrides

The Smoke Test

Failure Signatures

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