Researchers from Mistral AI have developed Mixtral 8x7B, a language model that utilizes the Sparse Mixture of Experts (SMoE) model with open weights. This decoder model has been licensed under the Apache 2.0 license, and it functions as a sparse network of a mixture of experts.
Mixtral 8x7B offers exceptional performance due to its unique architecture. The feedforward block of Mixtral consists of eight different parameter groups, allowing for dynamic selection by the router network. This selection process enables each token to be processed by two parameter groups, known as experts, whose results are combined additively. This strategy significantly expands the model’s parameter space while maintaining cost and latency control.
One of Mixtral’s standout features is its effective use of parameters, leading to faster inference times at both small and large batch sizes. In benchmark tests, Mixtral has demonstrated comparable or superior performance to other prominent language models such as Llama 2 70B and GPT-3.5.
Mixtral outperforms Llama 2 70B in various tasks, including multilingual understanding, code production, and mathematics. The model can effectively extract data from its context window of 32k tokens, regardless of the data’s length and position within the sequence.
To ensure a fair evaluation, the research team conducted in-depth comparisons between Mixtral and Llama models across a wide range of benchmarks. These assessments covered math, code, reading comprehension, common sense thinking, world knowledge, and popular aggregated findings.
In addition to Mixtral 8x7B, the researchers also introduced Mixtral 8x7B – Instruct, a conversation model optimized for instructions. Through direct preference optimization and supervised fine-tuning, Mixtral – Instruct has outperformed other chat models such as GPT-3.5 Turbo and Llama 2 70B.
To encourage widespread access and diverse applications, both Mixtral 8x7B and Mixtral 8x7B – Instruct have been licensed under the Apache 2.0 license, allowing for commercial and academic use.
The development of Mixtral 8x7B highlights its exceptional performance and versatility across various domains. From math and code problems to reading comprehension, reasoning, and general knowledge, Mixtral demonstrates impressive capabilities as a powerful language model.
The source of the article is from the blog myshopsguide.com