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Machine Learning practitioner | Kaggler | Contributing to ๐ค| Author ZeroModels
Hi there, I'm Gitesh ๐

๐ค AI/ML Engineer, building & shipping models
๐งฉ Author of ZeroModels & Trinix
๐
2ร Kaggle Expert
๐ค Open-source contributor, Hugging Face & Keras
๐ Based in: Fullerton, USA
I'm an AI/ML engineer who builds open-source libraries that make modern models easy to use, from pretrained Keras 3 model collections to Triton-accelerated PyTorch layers. I work across computer vision, LLMs, RAG, and agentic AI, and I love turning research into clean, reusable code.
- ๐ฆ Author of ZeroModels, Keras 3 pretrained models across Vision, LLM, VLM, Depth & Speech
- โก Built Trinix, Triton-accelerated PyTorch layers with automatic fallback optimization
- ๐
2ร Kaggle Expert
- ๐ค Contributor to Hugging Face (Transformers, Diffusers) and the Keras ecosystem (
keras-cv, keras-hub)
- ๐ง Implemented SOTA vision models in Keras/TF: SegFormer, ConvNeXt, NextViT, YOLO
๐ ๏ธ Tools & Technology
๐ง ML Libraries & Performance
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Pretrained Models for Keras 3
An open-source Keras 3 collection of pretrained models spanning Vision, LLM, VLM, Depth, and Speech, backend-agnostic across TensorFlow, JAX, and PyTorch.
๐ฅ Highlights:
- One API, multiple modalities
- Runs on TF / JAX / PyTorch backends
- Clean, reusable model implementations
โก Tech Stack:
Keras 3 JAX TensorFlow PyTorch
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Triton-Accelerated PyTorch Layers
Drop-in PyTorch layers accelerated with custom Triton kernels, featuring automatic fallback optimization when a fast path isn't available.
๐ฅ Highlights:
- Custom fused Triton kernels
- Automatic fallback to native ops
- Plug-and-play with
torch.nn
โก Tech Stack:
Triton PyTorch CUDA
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Keras Implementation of AdEMAMix
A faithful Keras / TensorFlow implementation of the AdEMAMix optimizer, a mixture of two EMAs for faster, more stable training.
๐ฅ Highlights:
- Paper-accurate optimizer port
- Drop-in
keras.optimizers compatible
- Reproducible training recipes
โก Tech Stack:
Keras TensorFlow
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Run Hugging Face Models Locally
Browse the Hugging Face Hub, download with a click, and run detection, segmentation, depth, OCR, SAM, VLMs, ASR, TTS & diffusion fully locally. Your data never leaves your GPU.
๐ฅ Highlights:
- No cloud, no upload, 100% local
- Dozens of task types out of the box
- One-click model download & run
โก Tech Stack:
Hugging Face PyTorch Local Inference
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๐จ Vision Models & Apps
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Semantic Segmentation in TensorFlow
A clean TensorFlow / Keras implementation of SegFormer, efficient transformer-based semantic segmentation with hierarchical features.
๐ฅ Highlights:
- Transformer encoder + lightweight MLP decoder
- Reproducible training & inference
- Readable, well-structured code
โก Tech Stack:
TensorFlow Keras Transformers
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Drive Coding CLIs from Your Phone
Control any coding CLI from your phone via WhatsApp, Telegram, Discord, Slack, Teams, or Signal, code from anywhere, no laptop required.
๐ฅ Highlights:
- Works with 6+ chat platforms
- Bridges chat โ your terminal
- Code on the go
โก Tech Stack:
TypeScript Node.js Chat APIs
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ZeroModels: Open-source Keras 3 collection of pretrained models across Vision, LLM, VLM, Depth, Speech, and more
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</> Code from your phone control any coding CLI via WhatsApp, Telegram, Discord, Slack, Teams, or Signal
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Triton-accelerated PyTorch layers with automatic fallback optimization.
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A Keras 3 Implementation of AdEMAMix Optimizer
Python
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