README.md

---
license: cc-by-sa-4.0
language:
- en
tags:
- text-to-sql
- bird
- spider
- finer-sql
- training-data
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: bird_train_no_gen_table.tar.gz
---

# FINER-SQL — Training Resources Bundle

Convenience bundle of all the data assets needed to **train and evaluate**
[FINER-SQL](https://github.com/thanhdath/finer-sql) on BIRD-bench. Companion to
the [`thanhdath/FINER-SQL-3B-BIRD`](https://huggingface.co/thanhdath/FINER-SQL-3B-BIRD)
and [`thanhdath/FINER-SQL-3B-BIRD-no-gen`](https://huggingface.co/thanhdath/FINER-SQL-3B-BIRD-no-gen)
model cards.

> ⚠️ The training pipeline — single-GPU continual GRPO from
> `FINER-SQL-3B-BIRD` to a no-gen specialist — is documented in
> [`TRAIN_3B_BIRD_NO_GEN.md`](https://github.com/thanhdath/finer-sql/blob/dev/TRAIN_3B_BIRD_NO_GEN.md).
> This dataset gives you everything in §4 of that guide in one place.

## Files

| File | Size (compressed) | Size (extracted) | What is it |
|---|---|---|---|
| `bird_dev.tar.gz` | ~1.0 GB | ~3.5 GB | BIRD dev release: `dev_databases/`, `dev_gold.sql`, `dev.json`. Required by the official BIRD evaluator (`evaluation_bird_ex.py`) and by the SQL execution sandbox. |
| `bird_train.tar.gz` | ~10 GB | ~40 GB | BIRD train databases (`train_databases/`). Required for GRPO reward — the trainer executes both candidate and gold SQLs against these SQLites. |
| `bird_train_no_gen_table.tar.gz` | 3.4 MB | 60 MB | HuggingFace `Dataset` arrow file with **9 428 BIRD train prompts in vanilla / no-gen-table format** (top-30 GRAST columns + raw schema, no LLM-generated meanings). The training set used for the no-gen specialist. |
| `gt_rows_cache.pkl.gz` | 17 MB | 76 MB | Pickled `{(dataset, db_id, gold_sql): rows}` cache of executed gold SQLs for both BIRD train and dev. Speeds up the first 1–2 epochs of GRPO reward computation by 5–10× (no need to re-execute every gold). |

## Quick download (everything)

```bash
# Bulk download
huggingface-cli download thanhdath/finer-sql-training-bundle \
    --repo-type dataset \
    --local-dir ~/finer-sql-data --local-dir-use-symlinks False

# Layout it into the paths the training scripts expect
cd ~/finer-sql-data
mkdir -p ~/data/bird ~/data/grast-sql-data/data-train

tar xf bird_dev.tar.gz                    -C ~/data/bird/             # → dev/dev_databases, dev_gold.sql, dev.json
mkdir -p ~/data/bird/dev && mv ~/data/bird/dev_* ~/data/bird/dev/ 2>/dev/null || true
mkdir -p ~/data/bird/train && tar xf bird_train.tar.gz -C ~/data/bird/train/
tar xf bird_train_no_gen_table.tar.gz     -C ~/data/grast-sql-data/data-train/

gunzip -c gt_rows_cache.pkl.gz > ~/data/gt_rows_cache.pkl
```

After this, the canonical paths used by `train_bird_no_gen_table_v2.sh`,
`eval_final_3b_bird.sh`, and `reproduce.py` are populated:

```
~/data/bird/dev/dev_databases/             ← BIRD_DB_ROOT
~/data/bird/dev/dev_gold.sql                ← BIRD_GOLD
~/data/bird/dev/dev.json                    ← BIRD_DIFF
~/data/bird/train/train_databases/          ← used by db_execution/api.py
~/data/grast-sql-data/data-train/grpo_sql_writer_bird_train_no_gen_table/
~/data/gt_rows_cache.pkl
```

## Selective download (just what you need)

```python
from huggingface_hub import hf_hub_download

# Only the no-gen training arrow (60 MB extracted) — for re-running GRPO
hf_hub_download("thanhdath/finer-sql-training-bundle",
                "bird_train_no_gen_table.tar.gz", repo_type="dataset",
                local_dir="~/finer-sql-data")

# Only the GT cache (76 MB extracted) — speeds up reward calc
hf_hub_download("thanhdath/finer-sql-training-bundle",
                "gt_rows_cache.pkl.gz", repo_type="dataset",
                local_dir="~/finer-sql-data")

# Only the BIRD dev (3.5 GB extracted) — for evaluation
hf_hub_download("thanhdath/finer-sql-training-bundle",
                "bird_dev.tar.gz", repo_type="dataset",
                local_dir="~/finer-sql-data")
```

## Provenance

- **`bird_dev.tar.gz`** and **`bird_train.tar.gz`** are repackaged from the
  public [BIRD-bench](https://bird-bench.github.io/) dev/train releases. The
  archives are byte-identical to extracting the upstream zips. Original license
  applies.
- **`bird_train_no_gen_table.tar.gz`** is generated by the [GRAST-SQL](https://github.com/thanhdath/grast-sql)
  schema-linker pipeline on top of the BIRD train split. The `messages`
  column renders the chat template; `groundtruth_sqls` carries the (multiple)
  acceptable golds per question.
- **`gt_rows_cache.pkl.gz`** is built from BIRD train + dev gold SQLs by
  [`build_gt_cache.py`](https://github.com/thanhdath/finer-sql/blob/dev/build_gt_cache.py)
  (no human labour beyond the upstream gold SQLs).

## Reproducing FINER-SQL with this bundle

```bash
git clone https://github.com/thanhdath/finer-sql.git && cd finer-sql

export BIRD_DB_ROOT=~/data/bird/dev/dev_databases/
export BIRD_GOLD=~/data/bird/dev/dev_gold.sql
export BIRD_DIFF=~/data/bird/dev/dev.json

# Stand up the SQL executor sandbox (point it at ~/data/bird/{train,dev})
cd db_execution && uvicorn api:app --host 0.0.0.0 --port 8001 --workers 8 &
cd ..

# Continual GRPO from the joint BIRD+Spider checkpoint → no-gen specialist
bash train_bird_no_gen_table_v2.sh

# Evaluate every saved checkpoint
for s in 20 40 60 80 100; do
    bash eval_final_3b_bird.sh \
        output/grpo_bird_3b_no_gen_table_v2/checkpoint-$s \
        ~/data/grast-sql-data/data-train/.../bird_dev_top30_prompts_v2_no_gen_table \
        no_gen_step_$s 0
done
```

## Citation

```bibtex
@article{finer-sql-2026,
  title  = {FINER-SQL: Fine-grained reasoning rewards for small Text-to-SQL models},
  author = {Thanh Dat and others},
  year   = {2026},
}
```

BIRD-bench:

```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 Yang, Jiaxi and Li, Binhua and Li, Bowen and Wang, Bailin and Qin, Bowen and Cao, Ruiying and others},
  booktitle = {NeurIPS},
  year   = {2023}
}
```