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README.md
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# tinyrouter-m1
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Two **separate** Milestone-1 triage
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| **`5-domain`** | **5** | 328,282 | `math` / `code` / `knowledge` / `commonsense` / `instruction` over the full M1 pool |
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| **`20-domain`** | **20** | 5,000 | **exact** [GCI-Bench](https://huggingface.co/datasets/Glint-Research/GCI_Bench) topics |
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```python
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from datasets import load_dataset
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ds5 = load_dataset("James-Cuda/tinyrouter-m1", "5-domain", split="train")
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ds20 = load_dataset("James-Cuda/tinyrouter-m1", "20-domain", split="train")
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```
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Built by `scripts/build_tinyrouter_m1_slim.py`.
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# tinyrouter-m1
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Two **separate** Milestone-1 triage datasets. Same schema; different domain taxonomies.
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## Overview
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| config | #domains | train | test | total | difficulty levels present |
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| --- | ---: | ---: | ---: | ---: | --- |
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| **`5-domain`** | 5 | 311,868 | 16,414 | 328,282 | 1, 2, 3, 4, 5 |
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| **`20-domain`** | 20 | 4,750 | 250 | 5,000 | 2, 3, 4 |
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## Schema (both)
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| column | type | meaning |
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| --- | --- | --- |
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| `id` | string | stable row id (`source:split:idx`) |
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| `domain` | string | closed domain label for that config |
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| `difficulty` | int | 1–5 |
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| `prompt` | string | user / problem text |
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## `5-domain` — detailed
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Full Milestone-1 pool remeshed to 5 router buckets.
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**Difficulty:** 1=118, 2=92,844, 3=218,087, 4=14,812, 5=2,421
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| domain | description / sources | train | test | total |
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| --- | --- | ---: | ---: | ---: |
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| `math` | GSM8K, MATH-500, AIME, AQuA-RAT, … | 102,658 | 5,442 | 108,100 |
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| `code` | HumanEval, MBPP, BigCodeBench, … | 3,009 | 158 | 3,167 |
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| `knowledge` | MMLU, MMLU-Pro, GPQA, ARC, TruthfulQA, GCI, … | 134,537 | 7,109 | 141,646 |
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| `commonsense` | HellaSwag | 57,027 | 2,923 | 59,950 |
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| `instruction` | Dolly-15k, IFEval | 14,637 | 782 | 15,419 |
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## `20-domain` — detailed
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[GCI-Bench](https://huggingface.co/datasets/Glint-Research/GCI_Bench) rows only
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(GPL-3.0). `domain` = official GCI `topic`; topic labels from upstream `topicLabel`.
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Difficulty: easy→2 / medium→3 / hard→4.
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**Difficulty:** 2=1,447, 3=2,278, 4=1,275
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| domain | topic label | train | test | total | difficulty (2/3/4) |
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| --- | --- | ---: | ---: | ---: | --- |
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| `agriculture` | Agriculture & Crops | 228 | 22 | 250 | 75/120/55 |
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| `archaeology` | Archaeology | 237 | 13 | 250 | 69/121/60 |
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| `architecture` | Architecture & Construction | 237 | 13 | 250 | 76/121/53 |
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| `astronomy` | Astronomy & Space | 242 | 8 | 250 | 69/115/66 |
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| `automotive` | Automotive Mechanics | 241 | 9 | 250 | 60/130/60 |
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| `aviation` | Aviation | 241 | 9 | 250 | 74/117/59 |
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| `chemistry` | Chemistry Lab Processes | 242 | 8 | 250 | 72/114/64 |
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| `cooking` | Culinary Science | 241 | 9 | 250 | 82/105/63 |
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| `energy` | Renewable Energy | 232 | 18 | 250 | 81/103/66 |
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| `finance` | Personal Finance | 239 | 11 | 250 | 74/104/72 |
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| `gardening` | Home Gardening | 235 | 15 | 250 | 75/110/65 |
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| `hardware` | Computer Hardware | 237 | 13 | 250 | 66/113/71 |
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| `marine` | Marine Biology | 237 | 13 | 250 | 87/94/69 |
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| `music` | Music Theory & Instruments | 239 | 11 | 250 | 82/113/55 |
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| `photography` | Photography | 236 | 14 | 250 | 60/111/79 |
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| `physiology` | Human Physiology | 235 | 15 | 250 | 66/113/71 |
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| `sports` | Sports Training | 233 | 17 | 250 | 76/120/54 |
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| `textiles` | Textile & Fashion | 245 | 5 | 250 | 75/108/67 |
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| `weather` | Weather & Climate | 239 | 11 | 250 | 70/109/71 |
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| `wildlife` | Wildlife & Animal Behavior | 234 | 16 | 250 | 58/137/55 |
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## Load
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```python
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from datasets import load_dataset
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ds5 = load_dataset("James-Cuda/tinyrouter-m1", "5-domain", split="train")
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ds20 = load_dataset("James-Cuda/tinyrouter-m1", "20-domain", split="train")
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print(ds5[0]["domain"], ds5[0]["difficulty"])
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print(ds20[0]["domain"], ds20[0]["difficulty"])
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```
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Built by `scripts/build_tinyrouter_m1_slim.py`.
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