README.md

---
base_model: microsoft/Phi-3-mini-4k-instruct
library_name: transformers
model_name: schema-forg-ai
tags:
- generated_from_trainer
- sft
- trl
- lora
licence: license
license: apache-2.0
datasets:
- edsoncarvalhointuria/erp-schema-forg-dataset
language:
- en
---

# Schema Forg AI - LoRA

## Overview

Schema Forg AI is a LoRA adapter trained on top of **Microsoft Phi-3 Mini 4K Instruct** to specialize the model in API schema generation and conversion tasks.

Rather than creating a new general-purpose language model, this LoRA teaches the base model how to generate structured API specifications with a strong focus on developer workflows.

## Capabilities

After loading this adapter, the model becomes significantly better at tasks such as:

- Generate Zod schemas
- Generate OpenAPI JSON specifications
- Generate OpenAPI YAML specifications
- Convert JSON structures into API schemas
- Produce structured API documentation
- Generate consistent backend-oriented outputs

## Dataset

The adapter was trained using the **ERP Schema Forg Dataset**, a synthetic dataset composed of thousands of examples covering schema generation and conversion tasks, including:

- JSON → Zod
- JSON → OpenAPI JSON
- JSON → OpenAPI YAML
- API documentation generation
- Nested objects
- Arrays
- Validation rules
- Endpoint specifications

The dataset was created specifically to improve the model's understanding of structured API development.

## Base Model

- Microsoft Phi-3 Mini 4K Instruct

## Training

This adapter was trained using **Supervised Fine-Tuning (SFT)** with the **TRL** library.

Training focused exclusively on schema-related tasks while preserving the reasoning capabilities of the original Phi-3 Mini model.

## Usage

This repository contains **only the LoRA adapter**.

To use it, load the adapter together with the base model:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Phi-3-mini-4k-instruct"
)

model = PeftModel.from_pretrained(
    base_model,
    "edsoncarvalhointuria/schema-forg-ai"
)

tokenizer = AutoTokenizer.from_pretrained(
    "microsoft/Phi-3-mini-4k-instruct"
)
```

If you prefer a standalone model without requiring PEFT, a merged version is also available on the Hugging Face Hub.

## Intended Use

This LoRA is intended for:

- API development
- Backend engineering
- OpenAPI generation
- Zod schema generation
- Developer tooling
- Educational projects
- Research on LLM specialization

## Limitations

This adapter specializes the base model for schema-related tasks.

While the underlying Phi-3 model retains its general capabilities, this LoRA performs best when used for:

- OpenAPI
- Zod
- JSON Schema
- Structured JSON
- API documentation

It is not intended to replace a general-purpose instruction model.

## Framework Versions

- TRL: 1.8.0
- Transformers: 5.14.1
- PyTorch: 2.5.1 + CUDA 12.1
- Datasets: 5.0.0
- Tokenizers: 0.22.2

## License

Apache 2.0

## Author

Developed by **Edson Carvalho Inturia** as an educational and research project exploring:

- LoRA fine-tuning
- Synthetic dataset generation
- Large Language Models
- API schema generation
- OpenAPI tooling
- Zod schema generation

The goal of this project is to investigate how domain-specific LoRA adapters can significantly improve structured code generation while remaining lightweight and easy to deploy.


Cite TRL as:
    
```bibtex
@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}
```