Nirvana is a Specialized Genearlist Model with task-aware memory mechanism, linear time complexity, and test-time task information extraction.

Nirvana/
├── nirvana_backbone/          # Core model architecture and training
│   ├── train/                 # Training scripts and configurations
│   ├── eval/                  # Evaluation scripts and benchmarks
│   ├── modeling_transformer_rnn.py      # Nirvana model
│   ├── nirvana_1_3B.json                # Nirvana 1.3B configuration
│   ├── configuration_transformer_rnn.py # Nirvana configuration
│   ├── task_aware_delta_net.py          # Specialized Memory Updater
│   └── ttt_cross_layer.py               # Task-Aware Trigger (with cross-layer online gradient descent)
├── specialized_ability/        # Domain-specific capabilities
│   ├── MRI_reconstruction/     # MRI image reconstruction and analysis model
│   │   ├── model/              # Custom MRI reconstruction and analysis model
│   │   ├── dataset/            # MRI dataset handling
│   │   └── train/              # MRI-specific training
└── requirements.txt            # Python dependencies

• Python 3.10+
• CUDA 11.8+ (for GPU acceleration)
• Conda or Miniconda
• 8+ GPUs recommended for training

1. Clone the repository

   cd Nirvana

2. Create and activate conda environment

   conda create -n nirvana python=3.10
   conda activate nirvana

3. Install dependencies

   pip install -r requirements.txt

4. Install Flash Attention (optional, for enhanced performance in SWA)

   pip install flash-attn==2.7.0.post2 --no-build-isolation

• PyTorch: 2.5.0
• Transformers: 4.52.4
• Accelerate: 1.1.1
• Flash Attention: 2.7.0.post2
• Flash Linear Attention: 0.2.2
• FastMRI: 0.3.0 (for MRI datasets)
• WandB: 0.21.1 (for experiment tracking)

cd nirvana_backbone/train
bash train.sh

Training Configuration:

• Model: 1.3B parameters
• Data: FineWebEdu dataset
• Precision: BF16
• Distributed training with 8 GPUs
• Checkpointing every 1910 steps
• WandB integration for experiment tracking

cd specialized_ability/MRI_reconstruction
bash ./train/run_two_stage_training.sh

cd nirvana_backbone/eval

# In-context learning evaluation
bash eval_nirvana_1.3B-icl.sh

# Long sequence evaluation
bash eval_nirvana_1.3B-longbench.sh

# Commonsense reasoning evaluation
bash eval_nirvana_1.3B-commonsense.sh

# NIAH evaluation
bash eval_nirvana_1.3B-niah.sh

Supported Benchmarks:

• S-NIAH
• LongBench
• Commonsense reasoning tasks
• FastMRI

The model configuration is defined in nirvana_1_3B.json:

{
    "hidden_size": 2048,
    "num_heads": 16,
    "num_hidden_layers": 22,
    "max_position_embeddings": 32768,
    "vocab_size": 32000,
    "concept_dim": 64,
    "logit_dim": 32,
    "window_size": 2048
}

1. Create a new directory under specialized_ability/
2. Implement your custom models in the model/ subdirectory
3. Add dataset handling in the dataset/ subdirectory
4. Create training scripts in the train/ subdirectory
5. Update the main __init__.py files to register your models

• Task-Aware Delta Network: Implement custom delta functions in task_aware_delta_net.py
• Cross-Layer Connections: Modify ttt_cross_layer.py for custom layer interactions
• Transformer Variants: Extend modeling_transformer_rnn.py for new architectures

• Parameters: 1.3B
• Training Context Length: 4096 tokens
• Training Precision: BF16
• Acceleration: Flash Linear Attention
• Parallelism: Data, tensor, and sequence parallelism support

• Selective Recompute: Configurable gradient checkpointing
• Mixed Precision: BF16 training with automatic mixed precision
• Distributed Training: Multi-GPU and multi-node support
• Memory Optimization: Efficient memory management with FSDP

• MRI Reconstruction: Fast and accurate MRI image reconstruction from undersampled k-space data
• Report Generation: Automated medical report generation from MRI
• Multi-modal Learning: Integration of k-space, imaging, and textual data

• Language Understanding: Strong performance on specialized and general language tasks
• Task Adaptation: Efficient adaptation for specialized applications