🤖 TezzLLM Enterprise v2.0 — STABLE RELEASE

Secure Federated AI For Corporate Environments

Created by Rohit Pathak Architect & Director · Creator of TezzNative and TezzLLM AI Suite TezzCorp Pvt Ltd, Bihar, India 🇮🇳
Access Download Portal Read Documentation

TPSL Licensed  ·  Zero Dependencies  ·  EULA available in docs

~1M
Parameters
0
Dependencies
64MB
Minimum RAM
TPSL
License
Quick Start

From install to
inference in 4 steps

TezzLLM runs entirely on your local machine. No cloud, no API keys, no data leaves your system.

1
Download & Extract
Get the executables package from the download portal
2
Tokenize Training Data
Run tezzllm_data.exe to build byte-pair vocab
3
Parallel Train & Merge
8-trainer federated ensemble — full CPU utilization
4
Start Chat Inference
Launch tezzllm_v2_engine.exe and start chatting
TezzLLM v2.0 — Deployment Terminal
C:\TezzLLM>tezzllm_data.exe
  [*] Tokenizing training corpus...
  [OK] 47,916 bytes → 12,243 tokens
 
C:\TezzLLM>build_v2.ps1
  [*] Launching 8 parallel trainers...
    [+] Trainer 0 PID=4821
    [+] Trainer 1 PID=5102
    ... 6 more trainers
  [OK] All trainers complete!
 
C:\TezzLLM>tezzllm_v2_merge.exe
  [OK] Federated merge → tezzllm_v2_merged.tezw
 
C:\TezzLLM>tezzllm_v2_engine.exe
  [OK] 1,082,496 parameters loaded.
 
USER: Hello, what is TezzLLM?
AI: TezzLLM is an on-device AI...
Enterprise Features

Designed for
corporate deployment

Every aspect of TezzLLM is engineered for security, control, and reliability in enterprise environments.

Confidential Source Code

Training data, weights, and custom configurations never leave your infrastructure. No cloud, no telemetry.

Zero Runtime Bloat

Single executable, 8.66MB weights, 64MB RAM minimum. Runs on any Windows x64 corporate workstation.

Federated Integration

Contribute organization-specific weights through our secure federated averaging protocol — no raw data shared.

Fully Configurable

Tune temperature, top-K sampling, repetition penalty, max generation length — all from command line flags.

Corporate-Grade Scaling

Parallel training across all CPU cores with federated merge. Scale to your hardware, not the cloud's.

Professional Updates

Versioned weight releases, TPSL license compliance, and dedicated support from the TezzCorp team.

Architecture

Technical specifications

Every parameter of TezzLLM was hand-tuned by Rohit Pathak for maximum efficiency at minimum hardware cost.

ParameterValue
ArchitectRohit Pathak, TezzCorp
LanguageTezzNative v1.0
LicenseTPSL v1.0 (Proprietary)
ArchitectureTransformer (Decoder-only)
Layers4
Embedding dim128
Attention heads4 (head dim = 32)
FFN dimension512 (SwiGLU)
NormalizationRMSNorm
Position encodingRoPE (Rotary)
Max context128 tokens
Vocabulary256 (byte-level)
Total parameters1,082,496
Weight file size8.66 MB (.tezw)
Precisionfloat64
Min RAM64 MB
How It Works

Federated
Training Process

1

Local Preparation

Install TezzLLM on your workstation. Configure your training corpus — no data leaves your system.

2

Tokenize & Train

Run the tokenizer and parallel trainer. 8 instances train simultaneously on all CPU cores.

3

Merge Weights

The federated merger averages all 8 trained models into one optimized weight file via FedAvg.

4

Submit Weights (Optional)

Optionally contribute your .tezw to the global model pool to improve future releases.

Ready to Deploy?

Enterprise AI,
on your terms

Deploy TezzLLM v2.0 in your corporate environment today. No cloud required. No data leaves your servers.

Download TezzLLM v2.0 Contribute Your Weights

Support: support@tezzcorp.com  ·  Docs: ai.tezzcorp.com/docs/