DHRUV
I teach small models what the giants know — then I put them to work.
I'm Dhruv, a software developer working where modern AI meets the enterprise back office. My core craft is knowledge distillation: transferring the capability of massive models into compact ones that run fast, private, and anywhere.
At Metavision Technology I ship that thinking into real businesses, building AI and agentic workflows on top of Tally and ERP systems used by logistics, biotech and finance companies. Legacy systems hold the knowledge; modern AI passes it on. The fresco above is the job description — one hand passing the spark to another.
The Distillation
My flagship work: compressing a fine-tuned GPT-2 Large into DistilGPT-2 without losing its mind. A pipeline of semantic filtering, dual-LLM deduplication, multi-model augmentation and dual-loss training: 774 million parameters taught into 82 million. Keep scrolling and watch it happen.
Smaller. Faster. Still smart.
ARKA‑BI In development
One centralized place for a business to understand itself. ARKA‑BI plugs directly into Tally and puts agents to work on the pain points every finance team knows too well: reporting by hand, answers buried in ledgers, problems discovered a month late.
Metavision Technology · Software Developer
Metavision blends legacy enterprise systems with modern AI: Tally, SAP B1 and MIS platforms on one side, agentic workflows on the other. I develop and maintain the systems that pull data out of Tally and put it to work: custom client systems, MIS analytics, internal data pipelines, and ARKA‑BI, our agentic reporting platform. The throughline from my research is the same: take heavyweight capability and make it run where the business actually lives.
Tally Solutions · AI/ML Intern
Engineered a teacher-student knowledge distillation pipeline, compressing a massive model's knowledge into a domain-specific Small Language Model (SLM) for accounting and finance. Reduced inference time by 40% while retaining 76% of the teacher's accuracy. Authored automated NLP preprocessing pipelines (Pandas, NLTK) for 3,000+ unstructured financial texts, boosting training efficiency.
Shortcut Learning in Deep Neural Networks
First-authored a paper on detecting, characterizing, and mitigating shortcut learning in automated Knee Osteoarthritis (OA) grading. Engineered a three-phase experimental framework utilizing a disentangled ResNet-18 architecture extended with a Gradient Reversal Layer (GRL). Successfully mitigated domain-sensitivity and shifted model focus to true diagnostic anatomical features.
Tools of the Trade
Languages: Python, SQL, JavaScript, C#, Java, C++, PHP
ML Frameworks: PyTorch, Hugging Face Transformers, Scikit-learn, Pandas, NumPy
Core Competencies: Small Language Models (SLMs), NLP, Knowledge Distillation, Agentic Workflows, Adversarial Disentanglement.
Engineering: MERN Stack, .NET, Tally / ERP Integration, Data Pipelines, Model Optimization.
What I do
Let's create something together.
Open to interesting problems in applied ML and enterprise software. The fastest ways to reach me: