White R.

Key Skills

Software

Label Studio

Top Subject Matter

Computer Vision (Traffic Sign Classification)

Computer Vision (Photovoltaic Cell Image Analysis)

Natural Language Processing (LLM Training)

Top Data Types

Image

Text

Top Task Types

Fine-tuning

Bounding Box

RLHF

Freelancer Overview

Manually authored thousands of complete training examples—ranging from simple to complex, and from linear to parallel execution—to train the RLHF-based ControlLLM. AI-training focus includes data types such as Image and Text and labeling workflows including Fine-tuning.

Labeling Experience

AI Training/Data Labeling – ControlLLM Corpus and Fine-Tuning

TextFine-tuning

I was a core contributor to the design of the corpus and course-learning modules for a lightweight, general-purpose LLM (ControlLLM). This involved selecting, curating, and annotating large-scale text data to refine language model capabilities. The process centered on text data selection, prompt engineering, and supervised fine-tuning for LLM improvement.• Designed and curated LLM training corpora • Conducted text annotation and prompt engineering • Led fine-tuning of ControlLLM's language models • Focused on supervised learning strategies for model enhancement

2024 - Present

AI Training/Data Labeling – Photovoltaic Cell Image Classification

ImageFine-tuning

I contributed to computer vision model training and optimization for photovoltaic cell image sets using PyTorch. The role involved data preparation, augmentation, and fine-tuning with DenseNet, ResNet, and RDFT models. I ensured labels were accurate and data quality high for model evaluation.• Worked with team on labeling photovoltaic cell images • Applied preprocessing and augmentation for better model training • Used Deep Learning architectures for fine-tuning and model optimization • Delivered labeled/curated dataset for evaluation and scoring

2023 - 2023

AI Training/Data Labeling – Vision Model Fine-Tuning (Kaggle Traffic Sign)

ImageFine-tuning

I participated in computer vision training using PyTorch on the Kaggle traffic sign image set. My responsibilities included fine-tuning and optimizing CNNs, Random Forests, and DNNs for enhanced model performance. The work focused on preparing and augmenting labeled image data for effective supervised learning.• Collaborated on model training for traffic sign recognition • Used PyTorch and various computer vision architectures • Optimized labeling and preprocessing for supervised learning • Achieved high performance score on Kaggle dataset

2023 - 2023

Education

Work History