README.md

---
language:
- en
tags:
- pytorch
- transformer
- language-model
- mixture-of-experts
- tree-of-thoughts
- neural-memory
datasets:
- openai/gsm8k
- cais/mmlu
- TIGER-Lab/MMLU-Pro
- openai/MMMLU
- MMMU/MMMU
- greengerong/leetcode
- LimYeri/LeetCode_Python_Solutions_v2
- newfacade/LeetCodeDataset
- deepmind/math_dataset
- google/IFEval
- Idavidrein/gpqa
- google/frames-benchmark
- camel-ai/math
- camel-ai/code
- microsoft/SCBench
- princeton-nlp/SWE-bench_Verified
- princeton-nlp/SWE-bench
- wikimedia/wikipedia
- HuggingFace/C4
- SamuelYang/bookcorpus
- sentence-transformers/codesearchnet
- openai/openai_humaneval

license: mit
pipeline_tag: text2text-generation
---

# 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

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

## Example Usage

```python
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](https://github.com/VishwamAI/VishwamAI)
- **Issues**: [GitHub Issues](https://github.com/VishwamAI/VishwamAI/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