---
license: apache-2.0
task_categories:
- text-generation
tags:
- insulin-control
- type-1-diabetes
- distillation
- chain-of-draft
size_categories:
- 1K<n<10K
---

# loop-qwen-v8 SFT dataset (Gemini insulin-control distillation)

**8,222** chat-format examples used to SFT `loop-qwen-v8` (Qwen3-4B insulin
controller distilled from Gemini-3-flash-preview). Each example is a closed-loop
dosing decision.

## Format (JSONL, one chat per line)
- **system**: controller spec (IOB-aware, Chain-of-Draft reason-before-act)
- **user**: patient metadata (age, weight, TDD, CF, IC, basal) + 6h history of CGM / insulin / carbs, as JSON
- **assistant**: JSON `{"reasoning": "...", "basal_chunk": [5], "bolus_chunk": [5]}` (reason-first, then the 5-step basal U/hr + bolus U action)

## Composition
- Gemini (rules-off + IOB + CoD) demonstrations on the **21 training patients** (held-out 9 excluded — leakage guard).
- **Multi-seed meals** (seeds 42/7/13/21) — the key ingredient that let the 4B student reach teacher parity without dosing oscillation.
- Built via `gen_teacher_data.py` -> `audit_to_sft.py` (see the loop-gpt repo).

## Companion model
`jxx123/loop-qwen-v8` - the 2.5 GB Q4 model trained on this data (held-out-9: TIR 72.4% vs Gemini 73.5%, survival 9/9).

## Research artifact
Simulation (simglucose) only. Not a medical device; do not use for real insulin dosing.