---
license: apache-2.0
base_model: Qwen/Qwen3-4B
library_name: peft
pipeline_tag: text-generation
tags:
- lora
- grpo
- text-to-sql
- hill-climb
- while-ai
datasets:
- while-ai/text-to-sql-shop
---

# text-to-sql-shop-r2

<!-- while-ai: where this fits -->
*Recipe: [recipes/04-train/text-to-sql](https://github.com/whilehq/whileai-sdk/tree/main/recipes/04-train/text-to-sql) · Collection: [Analyst](https://huggingface.co/collections/while-ai/analyst-6aada4c396dfe31da497862d)*

**One rung of the text-to-SQL hill climb on Qwen3-4B, thinking on, graded by executing the query.** Round `r2` starts from `r1`: GRPO 200 steps, lr 5e-5, beta 0.01. The eval rows for this round are the `eval-r2` config of the [text-to-sql-shop](https://huggingface.co/datasets/while-ai/text-to-sql-shop) dataset.

## Where it sits in the climb

81 held-out tasks, 4 samples each, temperature 0.7. The row in bold is this adapter.

| Round | From | Method | pass@1 (95% CI) | pass^4 | has_sql |
|---|---|---|---|---|---|
| r1 | base | GRPO 100 steps, lr 2e-5, beta 0.04, HF generate | 0.58 (0.52..0.64) | 0.29 | 0.89 |
| **r2** | r1 | GRPO 200 steps, lr 5e-5, beta 0.01 | 0.60 (0.54..0.67) | 0.34 | 0.90 |
| sft-think | base | self-distillation: 199 verified traces, hosted SFT 2 epochs | 0.60 (0.54..0.67) | 0.39 | 0.96 |
| r3 | r2 | GRPO 1,000 steps, lr 2e-5, beta 0.01, vLLM generation, 8 prompts per generate | 0.61 (0.56..0.67) | 0.29 | 0.86 |
| r4 | r3 | GRPO 1,000 more steps, same settings | 0.61 (0.54..0.67) | 0.32 | 0.85 |

Rounds r1 to r4 are within each other's intervals: two thousand GRPO steps did not move pass@1 on this holdout. The move came in r5, from a re-cut prompt band and masked truncation, and is the `text-to-sql-shop-r5` repo. The recipe README explains what each round changed and why.

## Arms in this repo

The root holds the arm the recipe README's headline number reports. Every other arm is a subfolder named after it. `checkpoints/` never ships.

| folder | arm |
|---|---|
| `.` | round `r2` adapter |

## Load

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B")
model = PeftModel.from_pretrained(base, "while-ai/text-to-sql-shop-r2")  # the headline arm
```


## Reproduce

```bash
git clone https://github.com/whilehq/whileai-sdk && cd whileai-sdk/recipes/04-train/text-to-sql
modal run train_grpo_modal.py --run-name t2s-r1
```

The recipe README pins the seed, the library versions and the GPU, and its Checks table says what the eval verified. Read the Learned section before quoting a number from this card.