---
license: cc-by-sa-4.0
pretty_name: Tiny Aya SQL agent - BIRD Mini-Dev evaluation trajectories
language:
- en
tags:
- text-to-sql
- bird
- evaluation
- trajectories
- agent
configs:
- config_name: bird_scores
  default: true
  data_files:
  - split: base
    path: runs/final-bird-sql-20261109-02/bird-base/metrics.jsonl
  - split: a1500
    path: runs/final-bird-sql-20261109-02/bird-a1500/metrics.jsonl
  - split: b
    path: runs/final-b-bird-sql-20261110-01/bird-b/metrics.jsonl
---

# Tiny Aya SQL agent: BIRD Mini-Dev evaluation records

Per-question scores and full agent trajectories for the untuned **Tiny Aya Global**, **A1500** and
**B** on 493 BIRD Mini-Dev questions (SQLite).
- **Use:** BIRD was used for the final evaluation only. It was never used for training, tuning or
  choosing a model.
- **Licence:** this dataset is kept separate because BIRD's licence is share-alike.

- Models: [B](https://huggingface.co/satish9177/tiny-aya-sql-agent) and
  [A1500](https://huggingface.co/satish9177/tiny-aya-sql-agent-a1500)
- Main data and logs: [satish9177/tiny-aya-sql-agent-data](https://huggingface.co/datasets/satish9177/tiny-aya-sql-agent-data)
- Code: [github.com/satish9177/aya-db-agent](https://github.com/satish9177/aya-db-agent)
- Write-up: [Teaching Tiny Aya to query databases (HF Article)](https://huggingface.co/blog/satish9177/teaching-tiny-aya-to-query-databases-what-fine-tun)

## Scores (strict; 493 of 500 questions)

| Model | Strict | Set |
|---|---:|---:|
| Tiny Aya Global, untuned | 0 (0.0%) | - |
| A1500 | 8 (1.6%) | 8 |
| **B (submitted)** | **7 (1.4%)** | 8 |

- **Exclusions:** seven questions were excluded before any model use, because their reference query
  exceeded the execution cap. They are listed in `eval-inputs/data/bird/final-v1/exclusions.jsonl`.
- **Main failure:** the models mostly run out of their eight tool steps on BIRD's large schemas.

## Contents

| Path | Contents |
|---|---|
| `eval-inputs/data/bird/final-v1/tasks.jsonl` | the 493 questions in our tool format, with BIRD's evidence notes where present, reference SQL and recomputed gold rows |
| `runs/final-bird-sql-20261109-02/` | Base and A1500: scores, trajectories, submissions, AuditKIT verdicts, job logs |
| `runs/final-b-bird-sql-20261110-01/` | B: the same |
| `MANIFEST.json` | SHA256 and size of every file |

**Databases are not included.** Get them from the official source (see below), then place them where the
task files point.

## About these records

- **Edits for publication:** personal details were removed after the runs.
  - `hf-user` stands for the HF account a job ran on.
  - Job links are reduced to job IDs.
  - Hashes inside the records refer to the files as they were when each job ran.
- **Sample rows:** they are shown when the agent describes a table, and they come from BIRD's public
  databases unchanged. One of them is an email address in a public user profile.
- **Difficulty labels:** the labels come from the source file as-is. That file labels 460 of 500
  questions "challenging", unlike BIRD's published split, so we report BIRD only overall.

## Licence and credits

- **Licence:** CC BY-SA 4.0, as these records are derived from BIRD Mini-Dev.
- **Model outputs:** the records also contain outputs of Tiny Aya Global (Cohere Labs, CC-BY-NC-4.0).
- **Source:** [`birdsql/bird_mini_dev`](https://huggingface.co/datasets/birdsql/bird_mini_dev), revision
  `5b256b5a9c17478f06ed6912612e9a14d17065c8`. The databases come from the card-linked official SQLite
  images.

Please cite BIRD:

```bibtex
@inproceedings{li2023bird,
  title     = {Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs},
  author    = {Li, Jinyang and Hui, Binyuan and Qu, Ge and others},
  booktitle = {Advances in Neural Information Processing Systems 36},
  year      = {2023}
}
```