VishwamAI

VishwamAI is an enhanced transformer model that combines several cutting-edge techniques to improve reasoning, memory retention, and computational efficiency.

Model Details

• Developers: VishwamAI Team
• Architecture: Enhanced Transformer with MoE
• Release Date: 2024
• Languages: English
• Framework: PyTorch
• License: MIT
• Model Type: Causal Language Model

Technical Specifications

• Parameters: 671B
• Context Length: 32,768 tokens
• Hidden Size: 8,192
• Attention Heads: 64
• Layers: 120
• Vocabulary Size: 64,000

Key Innovations

1. Differentiable Cache Augmentation

   • Enhances transformer's key-value cache with learnable embeddings
   • Enables asynchronous reasoning capabilities
   • Implements gated memory updating mechanism

2. Neural Long-Term Memory

   • Memory layers with read/write/forget gates
   • Multi-head memory attention mechanisms
   • Hierarchical memory organization

3. Tree of Thoughts Reasoning

   • Multi-path reasoning exploration
   • Beam search for solution paths
   • Intermediate step evaluation

Training Data

The model is being trained on a diverse set of datasets:

1. GSM8K

   • Grade school math word problems
   • Tests mathematical reasoning capabilities

2. MMLU (Massive Multitask Language Understanding)

   • Broad knowledge evaluation
   • Multiple academic and professional domains

3. MMLU-Pro

   • Professional and specialized knowledge
   • Advanced reasoning tasks

4. MMMLU (Massive Multi-task Multi-token Language Understanding)

   • Extended reasoning capabilities
   • Complex multi-step problems

Training Procedure

Hardware Requirements

• Minimum: Single NVIDIA A100 (80GB)
• Recommended: Multiple A100s with NVLink
• Distributed Training: Supported via FSDP

Software Requirements

• PyTorch >= 2.0
• CUDA >= 11.8
• [Optional] NCCL for distributed training

Optimization

• FP8 precision training
• Fully Sharded Data Parallel (FSDP)
• Gradient checkpointing
• Mixed precision training
• CPU offloading capabilities

Intended Use

This model is designed for:

• Research in language model capabilities
• Development of reasoning-enhanced applications
• Exploration of memory-augmented architectures

Primary Intended Uses

1. Research and Development

   • Study of neural memory mechanisms
   • Investigation of reasoning capabilities
   • Architecture optimization research

2. Educational Applications

   • Mathematical problem solving
   • Complex reasoning tasks
   • Knowledge retrieval and application

Out-of-Scope Uses

• Production deployment (currently in research phase)
• Safety-critical applications
• Real-time applications requiring low latency

Evaluation Results

Currently in training and evaluation phase. Initial metrics will be published after completion of training.

Limitations

1. Current Development Status

   • Training in progress
   • Performance metrics are preliminary
   • Features under active development

2. Technical Limitations

   • High computational requirements
   • Large memory footprint
   • Complex deployment needs

3. Capability Limitations

   • Reasoning capabilities still being optimized
   • Memory mechanisms under refinement
   • Limited multilingual support

Bias and Ethics

• Model is currently in research phase
• Full bias evaluation pending
• Not recommended for production use
• Safety measures being implemented

Environmental Impact

Working to minimize environmental impact through:

• Efficient training procedures
• Optimized architecture
• Resource-aware deployment options

Citation

@software{vishwamai2024,
  author = {Kasinadhsarma},
  title = {VishwamAI: Enhanced Transformer with Advanced Reasoning Capabilities},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/VishwamAI/VishwamAI}
}

Example Usage

from vishwamai.model_utils import load_model

# Load model
model = load_model("vishwamai/model", device="cuda")

# Generate output
input_ids = tokenizer.encode("Solve this problem step by step:", return_tensors="pt")
output = model(input_ids)

Additional Information

• Repository: GitHub Repository
• Issues: GitHub Issues
• Documentation: under construction mode owe are devloping it

Acknowledgments

This project builds upon several research papers and open-source projects. We thank the authors and contributors of:

• Transformer architectures
• Mixture of Experts implementations
• Tree of Thoughts reasoning
• Neural memory architectures