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.
Profile →npx skills add wshobson/agents --skill quantized-export --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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). - **FP8 is the default on Hopper-class GPUs and newer.** It preserves near-bf16 quality at roughly half the memory, and it's the safe first choice whenever the target GPU supports it and no edge-device constraint applies. - **AWQ INT4 targets older GPUs** that predate FP8 hardware support. **GPTQ is superseded for new deployments**
- Format Map
- Worked Picks
- Workload Overrides
- The Smoke Test
- Failure Signatures
- Related Skills
python smoke_test.py "$EXPORT_PATH" \ eval/goldens.jsonl pre-export-outputs.jsonl non-zero exit on any pre/post mismatch
What does the quantized-export skill do?
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.
How do I install it?
Run `npx skills add wshobson/agents --skill quantized-export --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From wshobson/agents, a repository with 38,479 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.