This AI Paper Presents a Direct Experimental Comparison between 8B-Parameter Mamba, Mamba-2, Mamba-2-Hybrid, and Transformer Models Trained on Upto 3.5T Tokens

Here's the simplified version of the text: Large Language Models in NLP Value: Transformers are very effective in NLP due to their unique self-attention mechanism. Challenges with Self-Attention Layers Practical Solutions: Selective State-Space Models (SSMs) like Mamba have been developed to address the efficiency issues of self-attention layers. They reduce computational complexity and memory requirements. Comparison of SSMs and Transformers Insights: Recent studies show that SSMs can compete with or outperform Transformers in language modeling tasks, especially with larger models and datasets. Performance of Mamba and Transformer Models Findings: An 8-billion-parameter Mamba-2-Hybrid model outperformed the 8-billion-parameter Transformer on standard tasks, with an average improvement of 2.65 points. The hybrid model also demonstrated faster token generation during inference. Long-Context Capabilities Evaluation: The hybrid model continued to perform on par with or better than the Transformer on average across additional long-context tasks, demonstrating its effectiveness in handling longer sequences. AI Solutions for Business Benefits: AI can help businesses identify automation opportunities, define KPIs, select suitable AI solutions, and implement them gradually to evolve the company with AI. Practical AI Sales Bot Value Proposition: The AI Sales Bot from itinai.com/aisalesbot automates customer engagement 24/7 and manages interactions across all customer journey stages, redefining sales processes and customer engagement. List of Useful Links: AI Lab in Telegram @itinai – free consultation Twitter – @itinaicom