Revolutionizing Language Models with toCommon
Welcome to toCommon, a groundbreaking initiative focused on simplifying language models by reducing vocabulary complexity. Our mission is to enhance the efficiency of Large Language Models (LLMs) through innovative methodologies.
Key Highlights:
- Simplified Vocabulary: Introducing the Simple-Word Compound Substitution to lessen token redundancy while maintaining semantic meaning.
- Efficient Scaling: Leveraging transformer layer reuse for cost-effective training and enhanced domain specialization.
- Reliability Increase: Utilizing intentional overfitting and caching to improve accuracy and minimize hallucinations.
- Dataset Quality Improvement: Addressing flaws in training data to boost model performance.
As we innovate, we remain in the early stages of development, eager to collaborate with fellow researchers and developers. Join us to explore transformative approaches in AI.
🔗 Get Involved! Open issues, discuss, or connect with us directly. Together, let’s shape the future of AI!
