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kelsbeans/qwen3-1.7b-digestive-coach-n97
qwen3-1.7b-digestive-coach-n97 is a machine learning model from kelsbeans. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Unsloth QLoRA fine-tune of Qwen/Qwen3-1.7B on 97 rows of kelsbeans/digestive-coach-dataset (nested subset of one shuffle; fixed family-disjoint valid set of 81). Merged fp16 — loads with plain transformers, no peft. A…
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From the Hugging Face model README
Unsloth QLoRA fine-tune of Qwen/Qwen3-1.7B on 97 rows of
kelsbeans/digestive-coach-dataset
(nested subset of one shuffle; fixed family-disjoint valid set of 81).
Merged fp16 — loads with plain transformers, no peft. Adapter: kelsbeans/qwen3-1.7b-digestive-coach-n97-adapter.
Pinned for verification (R48/RUBRIC-09)
74163732c0527d55ae8a754204abb606c8f98ade75863851837a7cc853b7052704e8ef7b6f7f8690| metric | base Qwen3-1.7B | this model |
|---|---|---|
| parse rate | 12.5% | 55.0% |
| schema validity | 2.5% | 47.5% |
| spec adherence | 0.0% | 7.5% |
| field accuracy | 26.7% | 28.3% |
Reproduce: python eval.py --model kelsbeans/qwen3-1.7b-digestive-coach-n97 --base Qwen/Qwen3-1.7B --eval-set eval_scenarios.jsonl --max-new-tokens 1600
(harness in the project repo at the pinned eval-code hash; raw per-example
transcripts ship as JSONL in runs/).
Best prompted frontier cell on the same eval: 15% spec adherence — below the full-N checkpoints (n781 20%, n1102 25%), which beat that ceiling.
Not medical advice. Research artifact for a data→behavior fine-tuning project.