| title: Phi3 Text to SQL Studio | |
| emoji: 🗄️ | |
| colorFrom: indigo | |
| colorTo: purple | |
| sdk: docker | |
| pinned: false | |
| # Phi-3 Text-to-SQL Studio | |
| A fine-tuned **Phi-3-mini-4k-instruct** model (QLoRA LoRA adapter) for natural-language → SQL, | |
| served on **CPU** via **llama.cpp + a 4-bit Q4_K_M GGUF**, behind a Flask web UI with a schema | |
| sidebar, generated SQL, and live SQLite execution. | |
| - **Adapter (LoRA):** https://huggingface.co/Bhuvandesai/phi3-text-to-sql-adapter | |
| - **Quantized GGUF:** https://huggingface.co/Bhuvandesai/phi3-text-to-sql-gguf | |
| ## Highlights | |
| - Trained only **0.12% of params** (4.46M, a 9 MB adapter) with QLoRA in ~3 min on a 6 GB laptop GPU. | |
| - Held-out **execution accuracy 75%** (vs **41.7%** for the base model), 100% valid SQL. | |
| - **Q4_K_M GGUF** is **68.6% smaller** than f16 with no measured task-accuracy loss. | |
| > **Note:** runs on free `cpu-basic` (2 vCPU, no GPU). First load takes ~1–3 min (downloads the | |
| > model); each query takes ~30 s–2 min to generate. Submit a question and wait — it completes. | |
| A full write-up (fine-tuning + quantization + deployment deep dive, with all benchmarks) lives in | |
| [`docs/Phi3-Text-to-SQL-Finetuning-Quantization.md`](docs/Phi3-Text-to-SQL-Finetuning-Quantization.md). | |