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malaiwah/fruit-smoke
fruit-smoke is a machine learning model from malaiwah. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as apache-2.0.
Scratch publication target for the proxy-fruit cost-tiered training suite. Nothing in this repository is a usable pretrained model.
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Updated Aug 7, 2026
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From the Hugging Face model README
Scratch publication target for the proxy-fruit cost-tiered training suite. Nothing in this repository is a usable pretrained model.

The current full suite has 20 tests spanning:
Recorded results:
| environment | result |
|---|---|
| 4× RTX 6000 Pro spot | 20/20 PASS |
| home RTX 5090 subset | 17/17 PASS |
The full spot run completed on 2026-08-06. Its approximately $11 total included one debugging round; the clean suite itself is budgeted at about $4. Tier 0 is designed to run locally before any rental.
checkpoints/smoke3_ckpt.pt and checkpoints/smoke12_ckpt.pt: throwaway
full-state checkpoints from recovery/resume cases, about 2.42 GB each.logs/train_metrics.log: synthetic durable ledger used by the publisher
regression.val_progress.png: rendered ledger output.Artifacts may be replaced by later suite runs. Do not use this repository for model lineage or pin these checkpoint names as release inputs.
Test definitions, exact acceptance sentinels, cost tiers, and the latest run
ledger are in
SMOKE_PLAN.md.
The executable harness is smoke_all.sh in the same repository.