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edsoncarvalhointuria
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Model Details
Model Provider
edsoncarvalhointuria
Model Tree
microsoft/Phi-3-mini-4k-instruct
Input Modalities
Output Modalities
Supported Functionality
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.
After loading this adapter, the model becomes significantly better at tasks such as:
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:
The dataset was created specifically to improve the model's understanding of structured API development.
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.
This repository contains only the LoRA adapter.
To use it, load the adapter together with the base model:
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase_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.
This LoRA is intended for:
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:
It is not intended to replace a general-purpose instruction model.
Apache 2.0
Developed by Edson Carvalho Inturia as an educational and research project exploring:
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}}