jk200201
/

qwen2.5-coder-7b-bird-dpo

@@ -1,209 +1,148 @@
 ---
 base_model: Qwen/Qwen2.5-Coder-7B-Instruct
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct
 - dpo
-- lora
-- transformers
-- trl
 ---
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-### Framework versions
-- PEFT 0.18.1

 ---
+license: apache-2.0
 base_model: Qwen/Qwen2.5-Coder-7B-Instruct
 library_name: peft
 tags:
+- text-to-sql
+- sql
+- code-generation
+- bird
+- bird-sql
+- spider
 - dpo
+- qwen
+- qwen2.5
+- text-generation
+language:
+- en
+pipeline_tag: text-generation
+datasets:
+- birdsql/bird_mini_dev
 ---
+# Qwen2.5-Coder-7B BIRD-DPO
+A LoRA adapter for **Qwen2.5-Coder-7B-Instruct** fine-tuned for real-world Text-to-SQL with **DPO using frontier model disagreements**. Achieves **50.3% on the BIRD dev set** — a +23.3pp improvement over the base model on real-world database queries with messy schemas and domain-specific evidence.
+## Results
+| Benchmark | Metric | Score |
+|---|---|---|
+| **BIRD dev** (1,534 Q) | Result accuracy | **50.3%** |
+| **BIRD mini-dev** (500 Q) | Result accuracy | **44.4%** |
+| BIRD mini-dev — Simple | Result accuracy | 62.8% |
+| BIRD mini-dev — Moderate | Result accuracy | 38.8% |
+| BIRD mini-dev — Challenging | Result accuracy | 31.4% |
+| **Spider V1 dev** (1,034 Q, cross-eval) | Result accuracy | **75.9%** |
+**Cross-domain transfer**: trained purely on BIRD, this adapter scores **75.9% on Spider** — only 2.3pp below a Spider-specific adapter (78.2%). Strong evidence that DPO from frontier disagreements generalizes across SQL benchmarks.
+| Model | BIRD dev |
+|---|---|
+| Qwen2.5-Coder-7B base | 27.0% |
+| Qwen2.5-Coder-7B + SFT only | ~35% |
+| **Qwen2.5-Coder-7B + SFT + BIRD-DPO (this model)** | **50.3%** |
+## Quick Start
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+import torch
+BASE_MODEL = "Qwen/Qwen2.5-Coder-7B-Instruct"
+ADAPTER    = "jk200201/qwen2.5-coder-7b-bird-dpo"
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+bnb = BitsAndBytesConfig(
+    load_in_4bit=True, bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True,
+)
+model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL, quantization_config=bnb, device_map="auto", trust_remote_code=True
+)
+model = PeftModel.from_pretrained(model, ADAPTER)
+model.eval()
+schema   = "CREATE TABLE users (id INT, name TEXT, country TEXT);"
+question = "How many users are from Japan?"
+evidence = ""  # optional domain hint for BIRD-style queries
+prompt = f"""Convert the following natural language question into a valid SQL query.
+Database Schema:
+{schema}
+{f'External Knowledge:{chr(10)}{evidence}{chr(10)}{chr(10)}' if evidence.strip() else ''}Question: {question}
+Return only the SQL query with no explanation."""
+inputs = tokenizer.apply_chat_template(
+    [{"role": "user", "content": prompt}],
+    return_tensors="pt", add_generation_prompt=True
+).to(model.device)
+out = model.generate(inputs, max_new_tokens=256, do_sample=False, pad_token_id=tokenizer.eos_token_id)
+sql = tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True).strip()
+print(sql)
+```
 ## Training Details
+**The novel idea**: rather than human-annotated preferences, this model uses **automatically generated preference pairs from frontier model disagreements** — total cost: ~$25 of OpenRouter API calls.
+### Pipeline
+1. Run **Grok-4.1-fast** and **DeepSeek-V3.2** on BIRD train (9,428 questions). Both score ~53%.
+2. Compare results question-by-question. Where one model is right and the other wrong → preference pair (1,219 pairs from BIRD).
+3. SFT Qwen2.5-Coder-7B on BIRD train gold SQL (QLoRA r=32, α=64, NF4 4-bit, 3 epochs).
+4. DPO on the 1,219 clear-preference pairs on top of SFT (β=0.05, 1 epoch, cutoff=8192).
+### Hyperparameters
+| Stage | Setting |
+|---|---|
+| Quantization | 4-bit NF4 (QLoRA) |
+| LoRA rank | 32 |
+| LoRA alpha | 64 |
+| LoRA dropout | 0.05 |
+| Target modules | q/k/v/o_proj, gate/up/down_proj |
+| SFT epochs | 3, LR 2e-4 cosine |
+| DPO epochs | 1, LR 5e-5 cosine, β=0.05 |
+| Cutoff length | 8,192 tokens (H200 80GB) |
+### Hardware
+Northeastern Discovery HPC — single NVIDIA H200 80GB. Training time: ~1h 15min.
+## Key Finding — What Doesn't Work
+Initially trained with 4,677 pairs (1,219 clear-preference + 3,458 judge-resolved style pairs). This **regressed to 40.7%** (-9.6pp).
+**Lesson**: judge-resolved pairs where both models are correct but write different SQL carry zero correctness signal for BIRD's result-accuracy metric. They dilute training and hurt performance. Only use pairs where one model is right and one is wrong.
+## Limitations
+- BIRD test set was not used (hidden); evaluation is on the public dev set
+- Real-world databases with very long schemas (>8K tokens) get truncated
+- Optimized for SQLite syntax (BIRD format); MySQL/PostgreSQL outputs may need adaptation
+- Trained only on English questions
+## Related Models
+- **Spider version** of this approach: [`jk200201/qwen2.5-coder-7b-sql-dpo`](https://huggingface.co/jk200201/qwen2.5-coder-7b-sql-dpo) — 78.2% on Spider V1, also beats Grok-4 and DeepSeek V3
+## Citation
+If you use this model, please cite:
+```bibtex
+@misc{kothari2026qwenbirddpo,
+  author       = {Kothari, Jenish},
+  title        = {Qwen2.5-Coder-7B BIRD-DPO: Frontier-Disagreement DPO for Real-World Text-to-SQL},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/jk200201/qwen2.5-coder-7b-bird-dpo}},
+}
+```