Write model card for phonetic ASR evaluator model
Session from read-along-ai-agent-traces on Hugging Face by kingkw1 · MIT · condensed by Coders Talk
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3msession
0interventions
GOAL
Review how a fine-tuned MiniCPM phonetic evaluator was built and write an honest Hugging Face model card for it, deciding where in the repo it should live and whether it should be tracked by git.
- +0Prompt · +0Review how we created this model and help write a real model card for kingkw1/minicpm-phonetic-evaluator, place it appropriately in the repo, and tell me if it should be tracked by git.
- +0mAgent did · +0mTraced repo structure, git status, and read scripts for fine-tuning, baseline eval, and GGUF conversion to understand the training/eval pipeline.
- +0mAgent did · +0mFound provenance: Modal A100 QLoRA fine-tune of openbmb/MiniCPM-2B-sft-bf16 merged and converted to GGUF Q4_K_M; inspected the 50-example dataset and inference code.
- +0mAgent did · +0mNoticed the post-tuning evaluation reused the same 50 training examples rather than a held-out set, so flagged it as a smoke/provenance eval rather than a benchmark; checked git-tracked files and model artifact sizes.
- +1mAgent did · +1mChecked .gitignore rules and found the local HF model checkout under models/hf/... is ignored due to multi-GB weights, complicating where to place a tracked card.
- +1mAgent did · +1mWrote docs/model_cards/minicpm-phonetic-evaluator.md (254 lines) with explicit limitations section, and mirrored it into the ignored models/hf/.../README.md for HF upload readiness.
- +3mAgent did · +3mVerified the tracked card matches the mirrored HF copy byte-for-byte and is properly ignored under models/, then added a link to the card from the main README.md.
- +3mOutcome · +3mSession ended with a 254-line model card added at docs/model_cards/, mirrored into the ignored HF checkout, README.md updated with a link, and a recommendation that the card be git-tracked while weights/checkout stay ignored.
VERDICT
Next time I'd tell it upfront that the eval reuses training examples so it doesn't have to discover that itself, and that .gitignore excludes models/ so it can plan the tracked-vs-mirrored file split from the start. The correction that mattered wasn't really an intervention here, but the agent catching that the 'post-tuning eval' wasn't a held-out benchmark was the thing that kept the card honest instead of overstating results.