about me
i'm an ai and machine learning enthusiast with a degree in
computer science and engineering (CSE) with a specialization
in artificial intelligence and machine learning (AI/ML) and
hands-on experience in developing innovative solutions. my
passion lies in building intelligent systems that can
transform industries and improve user experiences. with a
diverse skill set in various programming languages and
frameworks, i can quickly adapt to new technologies and
deliver high-quality, impactful results.
i have successfully contributed to projects involving
generative ai and retrieval-augmented generation (RAG) for
large language models (LLMs), systems. my work has focused
on developing and fine-tuning ai models to enhance
performance and accuracy, particularly in real-world
applications. i'm dedicated to continuously learning and
growing as a developer, staying current with the latest
advancements in ai and related technologies.
in my free time, i enjoy gaming, working on my art, and
exploring new ways to use ai to allow for easier
self-expression through art.
i'm very much excited to further my expertise in generative
ai, machine learning, and other modern technologies, driving
innovation and making meaningful contributions to the field.
my links
projects
final year mini project
license plate detection and recognition using
YOLOv8 and easyOCR
this project is a part of our final year mini project
for computer science engineering. the goal of this
project is to design and develop a system that can
detect and recognize license plates in videos using
yolov8 and easyocr.
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license plate detection:
using YOLOv8, a real-time object detection system,
to detect license plates in videos.
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license plate recognition:
using easyOCR, an optical character recognition
system, to recognize the text in the detected
license plates.
final year major project
offline, open-source rag
retrieve, augment, and generate with open-source large
language models
this project provides a self-contained, offline platform
for retrieval augmented generation (rag) using
open-source large language models (llms). with this
project, you can ingesting files and leverage the power
of llms without relying on third-party services or
exposing sensitive data.
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offline embeddings & llms support:
no need for openai or other external services. our
platform uses open-source llms, ensuring your data
stays within your network.
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support for multiple sources:
ingest files from various sources, including:
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local files:
upload and process files from your local
machine.
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github repos:
ingest files from github repositories.
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websites: retrieve and process
web pages.
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streaming responses:enjoy real-time
responses from the model, enabling a more
interactive and conversational experience.
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conversational memory:
the model maintains context and understanding
throughout conversations, allowing for more coherent
and relevant responses.
benefits:
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maintain data confidentiality:
by keeping data within your network, you minimize
the risk of data breaches and ensure compliance with
data protection regulations.
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cost-effective:
no subscription fees or usage charges. run the model
on your own hardware or cloud infrastructure.
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flexibility:
integrate with your existing infrastructure and
workflows.
image comparator
a web-based tool for image manipulation tasks
image comparator is a versatile web-based tool for
various image manipulation tasks. it provides a
user-friendly interface for comparing, compressing,
resizing, cropping, and flipping images.
key features:
-
image comparison:
compare multiple images side by side and download
the combined result.
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image compression:
compress both JPG and PNG images to reduce file
size.
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image resizing:
resize images by specifying width, height, or scale
percentage.
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image cropping:
crop images with various aspect ratio options.
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image flipping:
flip images horizontally or vertically.
try it live!