TezzLLM Overview

TezzLLM is a high-performance proprietary enterprise language model written entirely in TezzNative — a next-generation systems programming language. Both the TezzNative language and the TezzLLM AI architecture were created by Rohit Pathak, Director of TezzCorp Pvt Ltd.

TezzLLM is licensed under the TezzCorp Private Source License (TPSL v1.0). The raw compiler and neural network source code are kept confidential and protected, while pre-compiled binaries are provided for secure, local, offline federated training and inference within enterprise intranets.

🌟 Key Pillars

1. Zero Runtime Dependencies: The tokenizer, parallel trainer, federated merger, and inference engine compile to self-contained, native .exe executables.
2. Pure Systems Code: No virtual machines or heavy garbage-collected runtimes. Everything is built using direct pointers, manual memory safety, and SIMD hardware vectorization.
3. Federated Learning by Design: Train models locally on your private data, then contribute only the trained mathematical weights back to the community for collective improvements.
4. Hardware Democratization: Accessible training. The tiny 1-million parameter model can be trained on a budget laptop using standard CPU cores.

🗺️ Documentation Directory

Use the sidebar navigation or select a guide below to learn how to operate the TezzLLM ecosystem:

• Quick Start: Compile, tokenize, train, and chat with a model in under 10 minutes.
• Training Guide: A complete walk-through of local and parallel training operations.
• Dataset Guide: Format and clean your text datasets for the byte-level tokenizer.
• Model Architecture: Understanding layers, SwiGLU, RMSNorm, and Rotary Position Embeddings.
• Weights Format: Complete breakdown of the binary .tezw weights layout.
• Hyperparameters Reference: Configurations and thresholds for training optimization.
• Scaling Roadmap: Our path from 1-million parameter Tiny v1 to 1-billion parameter Large models.