Large Concept Model (LCM) - Practical Implementation

This repository contains the implementation of my technical report paper "Towards Practical Concept-Based Language Models: An Efficiency-Focused Implementation". Our work demonstrates significant efficiency improvements in language processing through concept-based approaches.

Key Features

• 🚀 3.8× faster inference through sentence-level processing
• 📉 Linear memory scaling (O(n)) for long sequences
• 🌍 Multilingual support with minimal performance drop
• 💡 Adaptive concept quantization
• 🔄 Hybrid attention mechanism
• 📊 Geometric regularization for semantic fidelity

Installation

# Clone the repository
git clone https://github.com/arimanyus/large-concept-model
cd large-concept-model

# Create a virtual environment
python -m venv env
source env/bin/activate  # On Windows: env\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Quick Start

from lcm import ConceptModel

# Initialize model
model = ConceptModel.from_pretrained('lcm-base')

# Process text
concepts = model.extract_concepts("Your input text here")
output = model.generate(concepts)

Training

To train your own model:

python train.py \
    --data_path path/to/data \
    --batch_size 32 \
    --learning_rate 5e-5 \
    --max_steps 50000

Evaluation

Run evaluation on standard benchmarks:

python evaluate.py \
    --model_path path/to/model \
    --dataset cnn_dailymail

Model Architecture

Our implementation consists of three main components:

1. Concept Formation: Converts text to compressed concept embeddings
2. Concept Processing: 4-layer transformer with modified attention
3. Hybrid Generation: Combines concept and token-level processing

Hyperparameters

Key hyperparameters used in our experiments:

Parameter	Value
Learning Rate	5e-5
Batch Size	32
Warmup Steps	1000
Max Steps	50000
Weight Decay	0.01
Concept Dimension	768
Transformer Layers	4
Attention Heads	8
α (Hybrid Attention)	0.7

Results

Our model achieves:

• 82% ROUGE-L retention compared to BART
• 0.82 concept cluster purity
• 4% average performance drop in multilingual settings

Visualization

Generate concept space visualizations:

python visualize.py --embedding_dir path/to/embeddings

Citation

If you use this code in your research, please cite our paper:

@article{tiwari2024towards,
  title={Towards Practical Concept-Based Language Models: An Efficiency-Focused Implementation},
  author={Tiwari, Vivek K.},
  journal={arXiv preprint arXiv:2024.6154975},
  year={2024}
}

License

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

Contributing

We welcome contributions! Please check our contributing guidelines for details.

Acknowledgments

• IBM for technical guidance
• The authors of the original LCM paper
• The open-source NLP community

Contact

• Vivek K. Tiwari - [email protected] / [email protected]
• Project Link: https://github.com/arimanyus/large-concept-model