Understanding the Mixture of Experts Layer in Mixtral
#opensourcelanguagemodels #mixtral8x7b #sparsemixtureofexperts #aibenchmarks #transformerarchitecture #gpt35benchmarkanalysis #directpreferenceoptimization #multilinguallanguagemodels
https://hackernoon.com/understanding-the-mixture-of-experts-layer-in-mixtral
#opensourcelanguagemodels #mixtral8x7b #sparsemixtureofexperts #aibenchmarks #transformerarchitecture #gpt35benchmarkanalysis #directpreferenceoptimization #multilinguallanguagemodels
https://hackernoon.com/understanding-the-mixture-of-experts-layer-in-mixtral
Hackernoon
Understanding the Mixture of Experts Layer in Mixtral
Discover the architectural details of Mixtral, a transformer-based language model that employs SMoE layers, supporting a dense context length of 32k tokens.
Mixtral—a Multilingual Language Model Trained with a Context Size of 32k Tokens
#opensourcelanguagemodels #mixtral8x7b #sparsemixtureofexperts #transformerarchitecture #gpt35benchmarkanalysis #directpreferenceoptimization #multilinguallanguagemodels #hackernoontopstory
https://hackernoon.com/mixtrala-multilingual-language-model-trained-with-a-context-size-of-32k-tokens
#opensourcelanguagemodels #mixtral8x7b #sparsemixtureofexperts #transformerarchitecture #gpt35benchmarkanalysis #directpreferenceoptimization #multilinguallanguagemodels #hackernoontopstory
https://hackernoon.com/mixtrala-multilingual-language-model-trained-with-a-context-size-of-32k-tokens
Hackernoon
Mixtral—a Multilingual Language Model Trained with a Context Size of 32k Tokens
Discover Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model, trained with a context size of 32k tokens with access to 47B parameters.