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Mixtral of Experts - Summary

Mixtral of Experts - Summary

The paper introduces Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model that outperforms existing models like Llama 2 70B and GPT-3.5 on various benchmarks. It uses a routing network to select two experts per token, allowing access to 47B parameters while actively using only 13B, enhan

Language Models are Few-Shot Learners - Summary

The paper discusses the limitations of pre-trained language representations in NLP systems and the need for task-specific datasets and fine-tuning. The authors show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with pri