Property	Value
Base model	microsoft/Phi-3-mini-4k-instruct (3.8B params)
Fine-tuning method	QLoRA (4-bit NF4 + LoRA)
LoRA rank	r=16, alpha=32
Trainable parameters	8,912,896 (0.23%)
Training examples	76,577
Training hardware	NVIDIA RTX 5060 Ti 8GB
Training time	3 hours 2 minutes
Final train loss	0.5677
Max sequence length	256 tokens

How to Use

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

base_model = "microsoft/Phi-3-mini-4k-instruct"
adapter    = "Sid9797/querycraft-phi3-sql"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    quantization_config=bnb_config,
    device_map="cuda:0",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
)
model = PeftModel.from_pretrained(model, adapter)
model.eval()

prompt = '''### System:
You are a SQL expert. Given a database schema and a natural language question, generate a valid SQL query that answers the question. Output only the SQL query with no explanation.

### Schema:
CREATE TABLE employees (id INTEGER, name VARCHAR, department VARCHAR, salary FLOAT)

### Question:
What is the average salary by department?

### SQL:
'''

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=128, do_sample=False)

sql = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(sql.strip().split("\n")[0])
# SELECT AVG(salary) FROM employees GROUP BY department

Prompt Format

The model was trained on the Alpaca instruction format: System: You are a SQL expert. Given a database schema and a natural language question, generate a valid SQL query that answers the question. Output only the SQL query with no explanation. Schema: {CREATE TABLE statements} Question: {natural language question} SQL: {model generates SQL here}

Training Details

Dataset: b-mc2/sql-create-context — 78,577 examples with inline CREATE TABLE schemas
Train/Val split: 76,577 train / 2,000 validation (seeded shuffle)
Quantization: 4-bit NF4 with double quantization (bitsandbytes)
LoRA target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Optimizer: AdamW with cosine LR schedule, warmup_ratio=0.05
Effective batch size: 16 (batch_size=4, gradient_accumulation=4)
Packing: Enabled — short examples concatenated to fill 256-token sequences

Why the Base Model Scored 0% Exact Match

The base Phi-3 Mini, without fine-tuning, consistently wrapped SQL output in markdown code fences (```sql ... ```) and appended semicolons. This formatting breaks exact match evaluation even when the SQL logic is correct. Fine-tuning on consistently formatted examples eliminated this entirely.

Limitations

Optimised for single-table and simple multi-table queries
Schema must be provided as CREATE TABLE SQL statements
Best results on English-language questions
May struggle with highly complex nested subqueries

Sid9797/querycraft-phi3-sql

QueryCraft — Phi-3 Mini Fine-Tuned for Text-to-SQL

Evaluation Results

Model Details

How to Use

Prompt Format

Training Details

Why the Base Model Scored 0% Exact Match

Limitations

Links

Sid9797/
querycraft-phi3-sql