---
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- text-to-sql
- natural-language-to-sql
- phi-3
- unsloth
- gguf
- ollama
- logic
datasets:
- b-mc2/sql-create-context
language:
- en
library_name: unsloth
pipeline_tag: text-generation
---

# Phi-3-SQL-Expert (GGUF)

This is a specialized **Text-to-SQL** model fine-tuned from the **Microsoft Phi-3-mini-4k-instruct** architecture. It has been optimized using **Unsloth** to provide high-accuracy SQL generation while remaining lightweight enough to run on consumer hardware.

## Key Features
- **Architecture:** Phi-3-mini (3.8B parameters)
- **Quantization:** Q4_K_M GGUF (Optimized balance of speed and logic)
- **Training Technique:** Fine-tuned using Lora with [Unsloth](https://github.com/unslothai/unsloth).
- **Format:** GGUF (Ready for Ollama, LM Studio, and llama.cpp)

## Usage Instructions

### Ollama (Recommended)
To deploy locally:

1. Download the `.gguf` file.
2. Create a file named `Modelfile` and paste the following:
```dockerfile
FROM ./phi3-sql-expert.Q4_K_M.gguf

TEMPLATE """<|system|>
You are a helpful assistant that writes SQL queries. Given a user question and a table schema, output only the SQL code.<|end|>
<|user|>
{{ .Prompt }}<|end|>
<|assistant|>
"""

PARAMETER stop "<|end|>"
PARAMETER temperature 0.1
PARAMETER num_ctx 2048
```

3. Run ollama create sql-expert -f Modelfile

4. Run ollama run sql-expert


## Evaluation Data
The model was fine-tuned on the sql-create-context dataset, focusing on:

Mapping natural language to complex SELECT, WHERE, and JOIN statements.

Understanding table schemas provided in the prompt.

Maintaining strict SQL syntax.

## Recommended Settings
Temperature: 0.0 or 0.1 (SQL requires deterministic output).

Stop Tokens: Ensure <|end|> is set as a stop sequence to prevent "infinite looping" generation.

Context Window: 2048 or 4096 tokens.

Model Developer: mrcmilo

Base Model: Phi-3-mini-4k-instruct