Shizu0n/phi3-mini-sql-generator-merged

Phi-3 Mini SQL Generator — Merged Model

This is the merged (standalone) version of Shizu0n/phi3-mini-sql-generator.

The LoRA adapter weights have been merged directly into the base model, making this a standard AutoModelForCausalLM — no PEFT dependency required for inference.

Why two versions?

Repo	Purpose
Shizu0n/phi3-mini-sql-generator	Original QLoRA adapter — documents the training pipeline
Shizu0n/phi3-mini-sql-generator-merged	Merged standalone model — used for deployment and inference

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 model (fine-tuned)	73.5%

Training Details

• Dataset: b-mc2/sql-create-context — 1,000 train / 200 validation examples
• Method: QLoRA (4-bit NF4, LoRA rank 16, alpha 32)
• Hardware: NVIDIA T4 (Google Colab free tier)
• Training time: ~21 min
• Final train loss: 0.6526

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Shizu0n/phi3-mini-sql-generator-merged"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)
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))

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