A Deep Dive Into Generative's Newest Models: Gemini vs Mistral (Mixtral-8x7B)–Part I
Dec 27, 2023
auto_awesome
ML Solutions Architect Dat Ngo and Product Manager Aman Khan discuss the new models Gemini and Mixtral-8x7B. They cover the background and context of Mixtral, its performance compared to Llama and GPT3.5, and its optimized fine-tuning. Part II will explore Gemini, developed by DeepMind and Google Research.
Mixtral 8x7B from Mistral AI is a high-quality sparse mixture of experts model that outperforms Llama 2 70B and matches or outperforms GPT3.5 on most benchmarks.
Sliding window attention introduces a fixed window size that moves across the sequence, reducing computational resources and improving performance in large language models.
Deep dives
Group Query Attention: An Efficient Approach to Attention Mechanism
The podcast episode discusses the concept of group query attention as an efficient approach to attention mechanisms in large language models. Traditional attention mechanisms can be computationally intensive, but group query attention addresses this by grouping multiple queries together and computing the attention for the group simultaneously. This helps improve computational efficiency while maintaining accuracy. The episode also explores the challenges of training and optimizing models with group query attention.
Sliding Window Attention: Efficient Handling of Long Sequences
The podcast episode highlights the use of sliding window attention to address the computational challenges of processing long sequences in large language models. Traditional attention mechanisms have quadratic complexity, but sliding window attention introduces a fixed window size that moves across the sequence, reducing computational resources required. This linear complexity approach helps handle larger sequences more efficiently, resulting in improved performance and reduced computation costs.
BPE Tokenizer: Handling Out-of-Vocabulary Words and Improving Coverage
The podcast episode discusses the use of Byte-Pair Encoding (BPE) tokenizer to improve language model performance. Traditional tokenizers face challenges with out-of-vocabulary (OOV) words and domain-specific jargon. BPE tokenizer provides a middle ground between word-based and character-based tokenization, allowing better coverage, adaptability to different languages, and improved understanding of domain-specific concepts. This optimization helps enhance the performance and flexibility of large language models.
Dense Model Architecture vs. Mixture of Experts (MOE)
The podcast episode compares dense model architecture with mixture of experts (MOE) approach in large language models. Dense models involve all neurons processing each piece of information, which can be computationally intensive, slower, and more expensive. On the other hand, MOE architectures utilize specialized experts that handle specific concepts, reducing inference time and cost. MOE allows for a larger number of parameters with improved performance and efficiency. However, training and optimizing MOE architectures present additional challenges and complexities.
For the last paper read of the year, Arize CPO & Co-Founder, Aparna Dhinakaran, is joined by a Dat Ngo (ML Solutions Architect) and Aman Khan (Product Manager) for an exploration of the new kids on the block: Gemini and Mixtral-8x7B.
There's a lot to cover, so this week's paper read is Part I in a series about Mixtral and Gemini. In Part I, we provide some background and context for Mixtral 8x7B from Mistral AI, a high-quality sparse mixture of experts model (SMoE) that outperforms Llama 2 70B on most benchmarks with 6x faster inference Mixtral also matches or outperforms GPT3.5 on most benchmarks. This open-source model was optimized through supervised fine-tuning and direct preference optimization.
Stay tuned for Part II in January, where we'll build on this conversation in and discuss Gemini-developed by teams at DeepMind and Google Research.