Upload README.md

README.md

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+# LMCODE: Language Model with Memory CODE
+
+A memory-augmented language model with **dual memory systems**: long-term and short-term memory, inspired by recent research in memory-augmented neural networks.
+
+## Overview
+
+LMCODE (Language Model with Memory CODE) extends traditional transformer-based language models with sophisticated memory mechanisms that enable:
+
+- **Long-term memory**: Persistent storage of knowledge and experiences (10,000+ memory slots)
+- **Short-term memory**: Working memory for immediate context (similar to Transformer KV cache)
+- **Memory retrieval**: Efficient similarity-based retrieval from long-term memory
+- **Memory consolidation**: Automatic merging of similar memories to prevent redundancy
+- **Experience replay**: Training with mixed current data and retrieved memories
+
+## Architecture
+
+### Components
+
+1. **ShortTermMemory**: Recurrent memory module for immediate context
+   - Update gates for controlled memory modification
+   - Read/write projections for memory access
+   - Soft updates to prevent catastrophic forgetting
+
+2. **LongTermMemory**: Persistent key-value store for long-term knowledge
+   - 10,000+ memory slots per layer
+   - Importance-weighted retrieval
+   - Consolidation mechanism for similar memories
+   - FIFO storage with intelligent replacement
+
+3. **MemoryAugmentedLayer**: Transformer layer with integrated memory
+   - Self-attention mechanism
+   - Short-term memory integration
+   - Long-term memory retrieval with gating
+   - Feed-forward network
+
+4. **LMCODE**: Complete language model
+   - Multiple memory-augmented layers
+   - Token and position embeddings
+   - Language model head
+   - Autoregressive generation support
+
+## Quick Start
+
+### Installation
+
+```bash
+pip install torch numpy matplotlib
+```
+
+### Basic Usage
+
+```python
+from model_architecture import LMCODE, LMCODEConfig
+
+# Create configuration
+config = LMCODEConfig(
+    vocab_size=50257,
+    hidden_size=512,
+    num_layers=6,
+    num_heads=8,
+    short_term_memory_size=512,
+    long_term_memory_slots=10000
+)
+
+# Initialize model
+model = LMCODE(config)
+
+# Forward pass
+input_ids = torch.randint(0, config.vocab_size, (1, 10))
+outputs = model(input_ids, use_long_term_memory=True)
+
+# Generate text
+generated = model.generate(input_ids, max_length=100)
+```
+
+### Training
+
+```python
+from training import MemoryAwareTrainer, MemoryDataset
+
+# Create dataset
+train_data = create_synthetic_dataset(num_samples=1000, seq_len=50)
+train_dataset = MemoryDataset(train_data, memory_sample_ratio=0.2)
+
+# Create trainer
+trainer_config = {
+    'learning_rate': 1e-4,
+    'weight_decay': 0.01,
+    'gradient_clip': 1.0,
+    'memory_consolidation_interval': 1000,
+    'warmup_steps': 1000
+}
+
+trainer = MemoryAwareTrainer(model, trainer_config)
+
+# Train
+history = trainer.train(train_dataset, num_epochs=10, batch_size=32)
+```
+
+## Files
+
+- `model_architecture.py`: Core LMCODE model with memory modules
+- `training.py`: Memory-aware trainer with experience replay
+- `utils.py`: Memory analysis, visualization, and monitoring tools
+- `run_demo.py`: Complete demonstration of all features
+- `README.md`: Full documentation
+
+## Research Background
+
+Inspired by key papers:
+- **LongMem (2023)**: Augmenting LLMs with Long-Term Memory
+- **MemoRAG (2024)**: Dual-system RAG with Global and Local Memory
+- **CAMELoT (2024)**: Training-free Consolidated Associative Memory
+
+## License
+
+MIT License