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license: mit
language:
- en
task_categories:
- text-generation
tags:
- instruction-tuning
- tool-calling
- math
- dfm11
pretty_name: dfm11-mathagentic-tinygsm-python
---
# dfm11-mathagentic-tinygsm-python
English arithmetic word problems converted into native Python tool-call trajectories with precomputed tool responses and boxed final answers.
## Contents
- Rows: 367,749
- Shards: 4
- Format: deterministic gzip JSON Lines in `data/train-*.jsonl.gz`
- Schema: `tools`, four-message native tool trajectory, execution metadata,
stable source ID, source revision, and admission status
- Intended repository: `schneiderkamplab/dfm11-mathagentic-tinygsm-python`
Each row contains a user problem, an assistant tool call, a precomputed tool
response, and a terminal assistant response containing exactly one boxed final
answer. It is directly consumable by Mathagentic/HRM-Text's Gemma 4 native
tool-call rendering path; no executor is used during training.
## Upstream datasets
- `TinyGSM/TinyGSM`
## Selection and verification
Deterministic 1-in-20 source sample; restricted-Python validation and execution; independent Gemma 4 26B A4B semantic audit; accepted verdicts only.
The semantic audit covered 500,000 candidates and accepted 367,749 (73.55%). Rejected rows are not packaged.
The package contains source trajectories, not tokenized arrays or sampled epoch
indices. Packaging does not itself admit the dataset into a DFM11 data mix.
The approved DFM11 sampling policy is to deterministically cap to 50,000 distinct rows per epoch, with repeat
`1`. This policy does not remove rows from the published
source package.
## License and citation review
The upstream TinyGSM dataset declares the MIT license. Preserve its attribution.
## Validate
```bash
python validate_dataset.py
```
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