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:
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:
@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}
}