Upload validation-selected step 1300 LoRA adapter

README.md

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+---
+base_model: Qwen/Qwen3-4B-Instruct-2507
+library_name: peft
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+  - text-to-sql
+  - reinforcement-learning
+  - rlvr
+  - lora
+  - bird
+---
+
+# ReViSQL-Qwen3-4B
+
+LoRA adapter trained directly from
+[`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
+with RLVR on expert-verified BIRD-Platinum data. This is the validation-selected
+step 1,300 adapter from the
+[`bird-text2sql-rl`](https://github.com/ximosss/bird-text2sql-rl) project.
+
+## Results
+
+| Dataset / decoding | Exact execution accuracy |
+| --- | ---: |
+| Arcwise-Plat-SQL, greedy | 376/498 = 75.50% |
+| Arcwise-Plat-SQL, SC-16 | 414/498 = 83.13% |
+| Arcwise-Plat, greedy | 404/498 = 81.12% |
+| BIRD Full Dev, greedy | 961/1,534 = 62.65% |
+| BIRD Mini-Dev, greedy | 298/500 = 59.60% |
+
+Greedy uses temperature 0. SC-16 samples 16 candidates at temperature 1 and
+selects the majority execution result. The model is evaluated as a SQL agent
+with up to five rounds of read-only database interaction; see the source
+repository for the exact prompt, environment, and evaluation contract.
+
+## Loading the adapter
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model_id = "Qwen/Qwen3-4B-Instruct-2507"
+adapter_id = "ximosss/ReViSQL-Qwen3-4B"
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(base_model, adapter_id)
+```
+
+The adapter alone does not reproduce the benchmark protocol. Use the database
+tool environment and prompts in the source repository for evaluation.
+
+## Training details
+
+- Objective: CISPO with execution, VeriEQL, and evidence-process rewards
+- LoRA: rank 32, alpha 64; attention and MLP linear layers
+- Learning rate: `1e-5`
+- Rollout temperature: `0.8`
+- Training batch / group size: 64 / 16
+
+## References
+
+- Yuxuan Zhu, Tengjun Jin, Yoojin Choi, and Daniel Kang. 2026.
+  [Human-Level Text-to-SQL via Reinforcement Learning on Verified Data, Without Pipeline Engineering](https://arxiv.org/abs/2603.20004).
+- Yuxuan Zhu, Tengjun Jin, Yoojin Choi, and Daniel Kang. 2026.
+  [Putting Task Expertise into RL Achieves State-of-the-Art Performance on Text-to-SQL](https://thinkingmachines.ai/news/putting-task-expertise-into-rl/).

adapter_config.json

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+{
+  "peft_type": "LORA",
+  "task_type": "CAUSAL_LM",
+  "base_model_name_or_path": "Qwen/Qwen3-4B-Instruct-2507",
+  "r": 32,
+  "lora_alpha": 64.0,
+  "lora_dropout": 0.0,
+  "bias": "none",
+  "target_modules": [
+    "down_proj",
+    "gate_proj",
+    "k_proj",
+    "o_proj",
+    "q_proj",
+    "up_proj",
+    "v_proj"
+  ],
+  "modules_to_save": null
+}

adapter_model.safetensors

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+oid sha256:fbf9f12b9b7368e27bb7fa8b384aad6fd6037857337e2b6f9f28af54ed1ec9ea
+size 264300328