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Prepare, launch, and evaluate Kimi LoRA fine-tuning for Smithers TypeScript/JSX workflow authoring.
Passed 19 of 20 test cases with the skill, and 4 of 20 without it.
and 22 more in the full report
17 fixed4 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
We want to deploy an existing fine-tuned model `accounts/my-org/models/my-adapter` for production inference on Fireworks AI using the official Fireworks CLI tool. What exact subcommand creates the deployment?
| Checked | Without skill | With skill |
|---|---|---|
| Executes fctl create deployment to create the model deployment | Missed | Passed |
The instructions your model reads when the skill loads.
Build the corpus locally, establish a base-model score, run the smallest useful LoRA SFT, and promote it only when the held-out benchmark improves.
Run from the Smithers repository root:
node skills/train-kimi-smithers/scripts/prepare-dataset.mjs \
--out artifacts/kimi-smithers
node skills/train-kimi-smithers/scripts/validate-dataset.mjs \
artifacts/kimi-smithers/train.jsonl \
artifacts/kimi-smithers/validation.jsonl
The builder uses only canonical workflow files listed in
assets/source-manifest.json. It splits by source file, never by generated
prompt variant, so validation cannot contain another wording of a training
completion. Read methodology.md before changing
sources, variants, or the system prompt.
Read fireworks.md completely before account setup, upload, job creation, promotion, or deployment. Recheck every linked live model, price, and CLI page because provider support changes quickly.
After the account has an API key, score the public base model before training:
test cases passed, vs 4 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.