Avadhan Training Module
Train LLM models using the revolutionary Avadhan Hybrid Architecture. 8 parallel attention threads, orthogonalized subspaces, and O(N log N) complexity.
8 Ashta Slots
Parallel attention
O(N log N)
Efficient scaling
Orthogonal
Zero interference
Meta-Control
Buddhi controller
Quick Start Templates
Research Assistant
Trained to analyze academic papers, summarize findings, and answer research questions with citations.
Code Reviewer
Reviews code for bugs, security issues, and best practices. Provides actionable improvement suggestions.
Creative Writer
Generates creative content including stories, poems, and marketing copy with unique voice.
Customer Support
Friendly and helpful support agent trained to resolve issues and answer product questions.
Data Analyst
Analyzes data patterns, generates insights, and creates visualizations from structured data.
Multi-Task Agent
Advanced agent using Sahasra regime for handling 1000+ parallel context threads.
Create AI Agent
Upload Model
Drop your model file here or browse
Supports: ONNX, PyTorch, TensorFlow, SafeTensors
Create a project first to upload a model
Upload Dataset
Drop your dataset here or browse
Supports: JSON, JSONL, CSV, TSV, Parquet, TXT, Arrow
Create a project first to upload a dataset
Training Controls
Create a project first to start training
Ashta Slot Visualizer
Memory Hierarchy
M_E(t+Δ) = M_E(t) + C_W(M_W(t))
Consolidation: Working → Episodic → Semantic
Training Console
(0 entries)Training Metrics
Loss Breakdown
💡 Quick Tips
- • Start with Ashta regime (8 slots) for most tasks
- • Higher β values reduce interference but slow learning
- • Watch Thread Purity metric - aim for > 90%
🚀 Enable GPU Training
To use real GPU training, start the Python backend:
cd backend && pip install -r requirements.txt && python main.py