The Athena 2.0 Client Application is a robust and feature-rich developer version application built using Python. It is designed to process in finetuning Athena2.0 model in automated manner with various data processing tasks, including data finetuning, evaluation, and quality assurance. the ability to handle JSON-based data inputs seamlessly. Our end-to-end decentralized training system seamlessly integrates data ingestion, model training, and deployment in client side in privacy mode.
It leverages secure, peer-to-peer communication to ensure transparency, privacy, and model integrity. By orchestrating resources across multiple devices, the system efficiently scales to meet training demands while mitigating single points of failure, making it a robust solution for collaborative AI development. Our system goes beyond traditional training by incorporating an advanced large language model as a judge, which objectively scores our model improvements following fine-tuning. This LLM evaluates enhancements using sophisticated natural language understanding and performance metrics, ensuring that every iteration is rigorously assessed and optimized for excellence. Everything is end to end autmoated after user moves his or her data over to one folder.
Minimum: At least one high‑memory NVIDIA GPU with 24 GB VRAM (e.g. NVIDIA RTX 3090). Recommended: Multiple GPUs with 40+ GB VRAM (e.g. NVIDIA A100, RTX A6000) to speed up fine‑tuning and support distributed training.
A multi‑core processor (e.g. Intel Xeon or AMD Ryzen Threadripper with 9 or more cores) to manage data loading and preprocessing efficiently.
Minimum: 32 GB of RAM. Recommended: 64 GB or more, especially if working with large datasets.
A high‑speed NVMe SSD with at least 30 GB of storage, since model checkpoints, datasets, and logs can consume significant space.
A 64‑bit Linux distribution (Ubuntu 20.04 LTS or later is recommended).
CUDA version 11.x or later with a compatible cuDNN version as supported by your PyTorch installation.
Python 3.8 or later (Python 3.10 is often preferred). Use virtual environments (via venv or Conda) for dependency isolation.
https://drive.google.com/file/d/1FsRPemr8vrLdtTrSN10ZAk3F5RZ481uF/view?usp=sharing