Shivnath Tathe

About Shivnath Tathe

Shivnath Tathe is a Software Engineer and independent researcher based in Hyderabad, India, specializing in 4-bit neural network training, retrieval-augmented generation systems, and autonomous AI agent design. He works as a Software Engineer at ISG eSolutions, where he builds production multi-agent pipelines, RAG applications, and scalable Angular front-ends.

He graduated from Shivaji University in 2024. His research is published on arXiv and Zenodo, and his open-source models are available on Shivnath Tathe's Hugging Face profile. His code and projects are maintained on Shivnath Tathe's GitHub. See his research work below.

Work and Projects

• 2026
  Published on arXiv: True 4-Bit Quantized CNN Training on CPU

  This paper demonstrates that CNNs can be trained from scratch at true 4-bit precision on commodity CPUs, with no specialized hardware and no post-training tricks. It achieved 92.34% accuracy on CIFAR-10 (just 0.16% below full-precision baseline), 70.94% on CIFAR-100, and 83.16% on a consumer Android device in only 6 epochs, with 8x memory compression over FP32. The key contribution is tanh-based soft weight clipping as a novel quantization technique.
  Read the paper on arXiv

  4-bit QAT CNN PyTorch CIFAR-10
• 2026
  T4NT-0.5B-50M: Tanh 4-bit Neural Transformer scaled to 50M tokens

  The same 535M parameter decoder-only architecture as T4NT-0.5B, extended with training on 50M tokens of WikiText data. Uses tanh soft weight clipping throughout and achieves improved perplexity over the 15M-token baseline. Released under Apache 2.0.
  View T4NT-0.5B-50M on Hugging Face

  LLM 4-bit Transformer HuggingFace
• 2025
  T4NT-0.5B: Tanh 4-bit Neural Transformer, first run trained on 15M tokens

  A 535M parameter decoder-only transformer trained entirely from scratch using 4-bit quantization-aware training on a single NVIDIA T4 GPU in under 3 hours. Architecture uses RoPE, SwiGLU, and RMSNorm. Best validation perplexity of 93.04 on WikiText-103. Deployed size of only 255 MB in INT4. Over 200 downloads on Hugging Face.
  View T4NT-0.5B on Hugging Face

  LLM 4-bit QAT RoPE SwiGLU RMSNorm
• 2025
  T4NT-0.5B-Instruct: Instruction-tuned 4-bit language model

  Instruction-tuned version of T4NT-0.5B, fine-tuned to follow user prompts and instructions rather than purely completing text. Retains the 4-bit quantized weight structure with full instruction-following capability.
  View on Hugging Face

  Instruction Tuning 4-bit
• 2025
  AgentForge: Autonomous AI Agents that Build Their Own Tools

  The working implementation of the Zenodo paper on autonomous tool-creation. Agents are built with LangChain and CrewAI and dynamically generate new tools at runtime to handle unforeseen challenges, reducing human intervention in complex workflows.
  View on GitHub

  LangChain CrewAI Agentic AI
• 2025
  Published on Zenodo: Autonomous Tool-Creation in AI Agents

  A conceptual framework for AI agents capable of self-generating tools in real-time. Targets mission-critical environments including aerospace, medical emergencies, and defense. Explores theoretical foundations, potential applications, and open challenges in building self-evolving agentic systems that require no human intervention.
  Read the paper on Zenodo

  AI Agents Agentic AI Zenodo
• 2025
  DevShakti AI — OpenAI-Compatible LLM Platform

  A self-hosted LLM platform running a fine-tuned Qwen 3 model on a private VPS with OpenAI-compatible API endpoints, authentication, rate limiting, and real-time token streaming. Includes a Python SDK (devshakti-ai) with 300+ downloads and live documentation with an interactive playground. Reduced inference response time from 52s to 19s, a 73% improvement.
  View on GitHub

  Qwen3 FastAPI React Python SDK VPS
• 2025
  Genagent — AI Content Generation Platform

  A full-stack AI content generation platform capable of producing 12+ content types including blog titles, SEO keywords, and social media captions. Built with Next.js for SSR, Drizzle ORM with PostgreSQL for data handling, and Razorpay for credit-based payments. Achieved 30% faster page loads through backend optimisation.
  View on GitHub

  Next.js Gemini API PostgreSQL Razorpay Drizzle ORM
• 2025
  DevShakti — Offline AI Assistant for Android

  DevShakti is a fully offline Android app built with React Native, running GGUF-quantized LLMs entirely on-device with no cloud dependency and no data leaving the device. Features include document upload with on-device RAG, tool calling, multi-agent pipelines, and the ability to connect external apps directly into the assistant.
  View on GitHub

  React Native GGUF On-device RAG Tool Calling Multi-Agent Android
• 2024
  Joined ISG eSolutions as Software Engineer

  Building RAG-based knowledge base systems using FastAPI, OpenAI, and vector embeddings, serving 10+ business clients through a 3-stage retrieval pipeline. Migrated vector storage from Pinecone to Qdrant for cost efficiency, adopted by 20+ clients in production. Built AI workflow automation tools including an offline LLM SQL assistant and a travel itinerary generator that cut extraction time by 57%. Designing custom multi-agent pipelines using LangGraph and LangChain for production workflows.

  LangGraph LangChain RAG FastAPI Qdrant Angular
• 2024
  Graduated, Shivaji University, Kolhapur

  Computer science and software engineering.

Research by Shivnath Tathe

• 2026
  LACE: Loss-Adaptive Capacity Expansion for Continual Learning

  arXiv:2603.28611 · March 2026

  LACE introduces a loss-driven mechanism that dynamically expands neural network capacity during training. When sustained loss indicates insufficient capacity, new dimensions are added without labels, replay buffers, or external control.

  Results: Precise domain-boundary detection (100% precision), performance matching large fixed-capacity models, and unsupervised domain separation in GPT-2 activations.

  Continual Learning Dynamic Capacity Loss-Based Expansion Adaptive Models
• 2026
  True 4-Bit Quantized CNN Training on CPU: Achieving Full-Precision Parity

  arXiv:2603.13931 · March 2026

  This paper challenges the assumption that neural network training requires high precision and expensive hardware. Using a novel tanh-based soft weight clipping technique combined with symmetric 4-bit quantization, dynamic per-layer scaling, and straight-through estimators, a VGG-style CNN was trained entirely from scratch on commodity CPUs with no specialized kernels and no post-training quantization.

  Results: 92.34% accuracy on CIFAR-10 (0.16% below full-precision baseline), 70.94% on CIFAR-100, and 83.16% on a consumer Android device in 6 epochs. 8x memory compression over FP32. Source code, training logs, and model checkpoints are open-source.

  4-bit QAT PyTorch CNN CPU Training Tanh Clipping
• 2025
  Autonomous Tool-Creation in AI Agents: A Conceptual Framework for Self-Evolving Systems

  Zenodo · 2025

  This paper proposes an agent architecture that enables autonomous tool generation during execution, going beyond AutoGPT and LangChain-style agents restricted to predefined toolsets. The framework allows agents to identify gaps in their capabilities and synthesize new tools on the fly without human intervention.

  Key applications explored include autonomous diagnostics, emergency-response systems, aerospace, and defense. Implemented as AgentForge, an open-source project built on LangChain and CrewAI.

  Agentic AI LangChain Tool Synthesis Self-Evolving Systems

Technical Skills

Contact

Email   Email Shivnath Tathe

GitHub  Shivnath Tathe on GitHub

LinkedIn  Shivnath Tathe on LinkedIn

HuggingFace  Shivnath Tathe on Hugging Face

Medium  Shivnath Tathe on Medium

DevTo  Shivnath Tathe on DevTo