metadata
base_model: microsoft/Phi-3-mini-4k-instruct
library_name: peft
license: apache-2.0
language:
- en
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% |
Training Details
- Dataset: b-mc2/sql-create-context — 1,000 train / 200 validation examples
- Epochs: 3
- Effective batch size: 8
- Learning rate: 0.0002
- Hardware: NVIDIA T4 (Google Colab free tier)
- Training time: 21.2 min
- Final train loss: 0.6526
- Best checkpoint: step 250 (by eval loss)
LoRA Config
| Parameter | Value |
|---|---|
| Rank (r) | 16 |
| Alpha | 32 |
| Dropout | 0.05 |
| Target modules | ['down_proj', 'qkv_proj', 'gate_up_proj', 'o_proj'] |
| Quantization | 4-bit NF4 (QLoRA) |
How to Use
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
tokenizer = AutoTokenizer.from_pretrained("Shizu0n/phi3-mini-sql-generator", 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,
)
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))
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