VishwamAI/VishwamAi_Transformer

Vishwamai Transformer

Overview

Vishwamai Transformer is an advanced architecture designed to unify the principles of neural networks and symbolic reasoning. This model introduces innovations in multi-perspective attention, sparse attention, adaptive feed-forward networks, and contextual integration for applications in natural language processing, knowledge integration, and dynamic output generation.

Features

• Dynamic Positional Encoding: Incorporates positional awareness dynamically based on input sequences.
• Search Engine Integration:
  • Real-time search query generation.
  • Multi-source information retrieval.
  • Contextual scoring of retrieved information.
  • Dynamic knowledge injection into the transformer layers.
• Adaptive Layers:
  • Multi-perspective attention.
  • Sparse/Axial attention mechanisms.
  • Flexible feed-forward networks with adaptive depth and width.
  • Context-aware normalization techniques.
  • Residual connections enhanced with attention gating.
• Contextual Memory: Retains critical information across layers to enrich decoding.
• Dynamic Output Generation:
  • Adaptive beam search for refined outputs.
  • Reinforcement learning for optimization.
  • Search-informed refinement of results.
  • Context-driven humor and creativity layer for natural interaction.

Flowchart

graph TD
    A[Input Sequence] --> B[Dynamic Positional Encoding]

    subgraph "Search Engine Integration"
        SE1[Real-time Search Query Generation]
        SE2[Multi-Source Information Retrieval]
        SE3[Contextual Information Scoring]
        SE4[Dynamic Knowledge Injection]
    end

    B --> C[Encoder]
    SE1 & SE2 & SE3 --> SE4
    SE4 --> C

    C --> D1[Multi-Perspective Attention]
    C --> D2[Sparse/Axial Attention]
    C --> D3[Flexible FFN\nAdaptive Depth/Width]
    C --> D4[Adaptive Normalization\nContext-Aware]
    C --> D5[Residual Connection\nAttention-Gated]

    D1 & D2 & D3 & D4 & D5 --> E[Contextual Memory Layer]

    E --> F[Decoder]

    F --> G1[Multi-Perspective Attention\nDecoder Side]
    F --> G2[Humor and Creativity Layer\nContext-Integrated]
    F --> G3[Flexible FFN\nAdaptive Depth/Width]
    F --> G4[Adaptive Normalization\nContext-Aware]
    F --> G5[Residual Connection\nAttention-Gated]

    G1 & G2 & G3 & G4 & G5 --> H[Dynamic Output Generation]

    H --> I1[Adaptive Beam Search]
    H --> I2[Reinforcement Learning\nOptimization]
    H --> I3[Search-Informed Refinement]
    H --> I4[Contextual Humor and Creativity]

    I1 & I2 & I3 & I4 --> J[Output Sequence]

Getting Started

Installation

To use Vishwamai Transformer, ensure you have the following dependencies installed:

• JAX
• Flax
• Optax
• NumPy

pip install jax flax optax numpy

Example Usage

from vishwamai_transformer import VishwamaiTransformer
import jax
import jax.numpy as jnp

# Initialize the model
model = VishwamaiTransformer(d_model=512, num_heads=8, num_layers=6, mlp_dim=2048, dropout_rate=0.1)

# Input data
rng = jax.random.PRNGKey(0)
input_data = jnp.ones((1, 128, 512))  # Batch size x sequence length x embedding size

# Initialize parameters
params = model.init(rng, input_data)

# Forward pass
output = model.apply(params, input_data)

License

This project is licensed under the MIT License. See the LICENSE file for details.