A LoRA adapter for Qwen2.5-Coder-7B-Instruct fine-tuned with DPO that achieves 78.2% on Spider V1 dev — outperforming Grok-4 (73.7%) and DeepSeek V3 (71.8%) despite being a 7B model.

Results

Model	Spider V1 dev	Parameters
Qwen2.5-Coder-7B + Spider-DPO (this model)	78.2%	7B
Grok-4 (frontier baseline)	73.7%	unknown (very large)
DeepSeek-V3 (frontier baseline)	71.8%	671B (37B active MoE)
Qwen2.5-Coder-7B base	~50%	7B

Cross-benchmark transfer

Benchmark	Score
Spider V1 dev (in-domain)	78.2%

For real-world database queries (BIRD-style schemas with evidence), use the companion model: jk200201/qwen2.5-coder-7b-bird-dpo.

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-sql-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?"

prompt = f"""Convert the following natural language question into a valid SQL query.

Database Schema:
{schema}

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

Run Grok-4 and DeepSeek-V3 on Spider dev set (1,034 questions).
Compare against gold SQL question-by-question. Where one frontier model is right and the other wrong → preference pair (the correct SQL is "chosen", the wrong one "rejected").
SFT Qwen2.5-Coder-7B on Spider train gold SQL (QLoRA r=32, α=64, NF4 4-bit, 3 epochs).
DPO on 1,040 clear-preference pairs on top of SFT (β=0.1, 2 epochs).

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	2, LR 5e-5 cosine, β=0.1

Training data

jk200201/spider-dpo-1040 — 1,040 preference pairs built from Grok-4 vs DeepSeek-V3 disagreements on Spider dev.

Hardware

AWS EC2 g5.xlarge (NVIDIA A10G 24GB VRAM). Training time: ~3h total.

Limitations

Designed for Spider-style queries: academic-style English, clean schemas, single SQLite dialect
For real-world messy databases with domain knowledge ("BIRD-style"), use jk200201/qwen2.5-coder-7b-bird-dpo
4-bit quantized — for highest accuracy use bf16 base model
Trained only on English questions

Citation

bibtex

@misc{kothari2026qwenspiderdpo,
  author       = {Kothari, Jenish},
  title        = {Qwen2.5-Coder-7B Spider-DPO: A 7B Model that Beats Frontier Models on Spider via Frontier-Disagreement DPO},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/jk200201/qwen2.5-coder-7b-sql-dpo}},
}