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base_model: microsoft/Phi-3-mini-4k-instruct
library_name: peft
license: mit
language:
  - en
datasets:
  - b-mc2/sql-create-context
tags:
  - sql
  - code-generation
  - text-to-sql
  - phi-3
  - lora
  - qlora
  - fine-tuned
  - peft
pipeline_tag: text-generation

Phi-3 Mini SQL Generator (QLoRA Fine-tuned)

Fine-tuned version of Phi-3-mini-4k-instruct for natural language → SQL generation using QLoRA on a T4 GPU (Google Colab, ~20 min).

Evaluation — Base vs Fine-tuned

Evaluated on 200 held-out examples from b-mc2/sql-create-context.

Model	Exact Match
Phi-3-mini-4k-instruct (base)	2.0%
This adapter (fine-tuned)	73.5%

Exact match: normalized SQL comparison (lowercase, strip whitespace/semicolons).

Training Details

Dataset: b-mc2/sql-create-context — 1,000 train / 200 validation examples
Epochs: 3
Effective batch size: 8
Learning rate: 0.0002
Max sequence length: 512
Hardware: NVIDIA T4 (Google Colab free tier)
Training time: 21.2 min
Final train loss: 0.6526
Best checkpoint: step 250 (lowest eval loss — mild overfitting observed after epoch 2)

LoRA Config

Parameter	Value
Rank (r)	16
Alpha	32
Dropout	0.05
Target modules	`qkv_proj`, `o_proj`, `gate_up_proj`, `down_proj`
Quantization	4-bit NF4 (QLoRA)

How to Use

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

tokenizer = AutoTokenizer.from_pretrained(
    "microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True
)
base_model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Phi-3-mini-4k-instruct",
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
    attn_implementation="eager",
)
model = PeftModel.from_pretrained(base_model, "Shizu0n/phi3-mini-sql-generator")
model.eval()

prompt = (
    "Given the following SQL table, write a SQL query.\n\n"
    "Table: employees (id, name, department, salary)\n\n"
    "Question: What is the average salary per department?\n\nSQL:"
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.inference_mode():
    outputs = model.generate(**inputs, max_new_tokens=100, do_sample=False)
prompt_len = inputs["input_ids"].shape[-1]
print(tokenizer.decode(outputs[0][prompt_len:], skip_special_tokens=True))

The LoRA adapter weights have been merged into a standalone model at Shizu0n/phi3-mini-sql-generator-merged — no PEFT dependency required for inference.

Limitations

Fine-tuned on 1,000 examples — best suited for simple to medium complexity SELECT queries
Not tested on dialect-specific SQL (PostgreSQL/MySQL-specific functions)
May struggle with multi-table JOINs and nested subqueries

Shizu0n
/

phi3-mini-sql-generator