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mayankpuvvala/
lora-t5-pytorch-issues

Name: lora-t5-pytorch-issues
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Model Card for `mayankpuvvala/lora-t5-pytorch-issues`

This model represents the LoRA adapter weights trained on a custom dataset of PyTorch GitHub issues. It is intended to be used with the base t5-small model to generate issue bodies from titles.

Model Details

Model Description

Developed by: Mayank Puvvala
Model type: LoRA Adapter for Text-to-Text Generation
Language(s): English
License: MIT
Fine-tuned from model: t5-small

Model Sources

Repository: GitHub

How to Get Started with the Model

python

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from peft import PeftModel

base_model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
model = PeftModel.from_pretrained(base_model, "mayankpuvvala/lora-t5-pytorch-issues")
tokenizer = AutoTokenizer.from_pretrained("t5-small")

input_text = "Memory leak when using DataLoader with num_workers > 0"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

Custom dataset of PyTorch GitHub issues, comprising titles and corresponding bodies.

Training Procedure

Fine-tuned using PEFT with LoRA for 3 epochs.

Training Hyperparameters

Epochs: 3
Batch size: 8

Evaluation Metrics

ROUGE Precision: 53.12%
ROUGE F1 Score: 49.8%

mayankpuvvala/lora-t5-pytorch-issues

Model Card for mayankpuvvala/lora-t5-pytorch-issues