Jino Rohit
Deep Learning Engineer at Pi School
Chennai, Tamil Nadu, India
Summary
🤩
Rockstar🎓
Top SchoolJino Rohit is a Deep Learning Engineer with six years of experience specializing in computer vision and applied deep learning, currently based in Chennai. He has a strong competitive ML pedigree—Top 1% Kaggle Competitions Expert with multiple bronze medals and several competition wins—and has shipped production-focused models during senior and data scientist roles at Tata Communications and startups. Jino is an active open-source contributor to prominent projects like Hugging Face's PEFT library, where he improved adapter configuration and LoRA tooling for NER and evaluation workflows. His background spans internships across research and industry labs, giving him hands-on experience from model prototyping to deployment. Beyond code, he pairs a sports-team mindset (former u-19 football champion and college sports coordinator) with a scholarship-supported academic foundation in computer science. He enjoys working with images, translating visual data into robust, efficient ML solutions that bridge research and product needs.
6 years of coding experience
3 years of employment as a software developer
9.6 GRADE POINTS, 9.6 GRADE POINTS at The Indian School, BahrainBachelor's degree, Computer Science, Bachelor's degree, Computer Science at Kumaraguru College of Technology
Github Skills (7)
params10
transformers10
pytorch10
para10
python10
adapter9
llm8
Programming languages (5)
Github contributions (5)
huggingface/peft
Sep 2024 - Nov 2024
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Role in this project:
Backend Developer
Backend DeveloperContributions:2 reviews, 7 PRs, 72 comments in 1 month
Contributions summary:Jino contributed significantly to the `peft` library, focusing on enhancing the core functionality of the tuner utilities. Their work involved adding the capability to exclude specific modules during adapter configuration and implementing a new check to validate the target modules within the model. Furthermore, the user made improvements to the LoRA notebook for NER task, integrating the lm-eval-harness toolkit. These modifications enhance the flexibility and usability of the PEFT library.
parametermachine-learningfineparameter-efficientfine-tuning
JINO-ROHIT/JINO-ROHIT
Jun 2021 - Oct 2024
Contributions:23 pushes in 3 years 4 months
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