Mistral AI Unveils Mistral 7B
Mistral 7B defies the notion that more parameters necessarily mean better performance, showing that a 7.3B parameter model can outperform rivals with up to 34B parameters across multiple benchmarks.
Created on September 27|Last edited on September 27
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Mistral AI has released its latest model, Mistral 7B, boasting 7.3 billion parameters while delivering top-tier performance across various benchmarks. What sets Mistral 7B apart is its efficiency and adaptability, as it challenges the performance of much larger models such as Llama 2 13B and Llama 1 34B.
Key Features
High Performance: Mistral 7B excels in benchmarks ranging from commonsense reasoning to code-related tasks, surpassing much larger models like Llama 2 13B and approaching the performance of CodeLlama 7B.
Grouped-Query Attention (GQA): The model employs GQA to speed up inference times, allowing for faster responses.
Sliding Window Attention (SWA): To manage longer sequences without compromising on computational efficiency, the model uses SWA, which has a linear computational cost.
Performance Metrics
Mistral AI re-evaluated all models in a head-to-head comparison using their evaluation pipeline. The metrics included commonsense reasoning, world knowledge, reading comprehension, math, and code tasks. Mistral 7B exceeded expectations, even when compared to models with a much larger number of parameters.
Technical Nuances
The model's use of SWA provides a linear computational cost, while layered attention mechanisms allow information to travel further back in a sequence than initially suggested. Moreover, the SWA implementation, coupled with changes to FlashAttention and xFormers, results in a 2x speed improvement for sequences of length 16,000 with a window of 4,000.
Availability and Licensing
Released under the Apache 2.0 license, Mistral 7B can be freely used and deployed on various platforms, including AWS, GCP, and Azure. The model is also compatible with HuggingFace, and Mistral AI provides a fine-tuned version for chat applications to demonstrate the model's versatility.
The Prevailing Trend: More Compute, More Power
In the AI world, a prevalent trend has dominated the last few years: the larger the model and the more computational power thrown at it, the better the performance. This idea has largely fueled the race for ever-bigger models with increasingly gargantuan numbers of parameters. Mega-corporations and academic institutions alike have leaned into this, leading to breakthroughs and escalating costs, energy consumption, and barriers to entry.
Efficiency and Elegance
However, an alternative approach is starting to gain traction. The idea is simple but transformative: what if we could maintain or even surpass existing performance levels with models that are not only smaller but also more computationally efficient?
The release of Mistral 7B showed us that a 7.3B parameter model could not only compete with but often surpass models with 13B or even 34B parameters in benchmark tests. This efficiency could redefine what's possible with machine learning and natural language processing.
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