Row 57908

Row ID: 57908 | Dataset Entry | Axioma AXP Content Repository

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To understand MoE, we should first answer why we need such a system, especially in the context of LLMs? The scale of a model is one of the most important factors for judging the model quality. The mixture of Experts enables models to be pretrained with far less compute, which means we can dramatically scale up the model or dataset size with the same compute budget as a dense model. In particular, a MoE model should achieve the same quality as its dense counterpart much faster during pretraining.

Let’s understand the mechanism behind MOE.

# Full article: [https://medium.com/aiguys/moe-and-llm-merging-recipes-741b11da2b4c](https://medium.com/aiguys/moe-and-llm-merging-recipes-741b11da2b4c)

A standard transformer block contains the self-attention mechanism, followed by a feed-forward layer. Whereas in MoE, Sparse MoE layers are used instead of dense feed-forward network (FFN) layers. MoE layers have a certain number of “experts” (e.g. 8), where each expert is a neural network. In practice, the experts are FFNs, but they can also be more complex networks or even a MoE itself, leading to hierarchical MoEs!

**The experts help predict one token at a time**

**The router decides which expert is active at each token**

**Experts are combined through a gated linear combination to predict the next token**

Although MoEs provide benefits like efficient pretraining and faster inference compared to dense models, they also come with challenges:

* **Training:** MoEs enable significantly more compute-efficient pretraining, but they’ve historically struggled to generalize during fine-tuning, leading to overfitting. * **Inference:** Although a MoE might have many parameters, only some of them are used during inference. This leads to much faster inference compared to a dense model with the same number of parameters.

# Evolutionary Merging

# Parameter (PS) merging

This type of merging focuses on combining the weights of multiple models to form a single, more powerful model while maintaining the original neural network architecture. This approach involves integrating the parameters (weights and biases) of the models layer by layer.

# Data Flow Space (DFS) Merging

Data Flow Space (DFS) Merging optimizes the sequence in which data flows through the layers of different models. Unlike PS Merging, DFS Merging keeps the original weights intact and focuses on the data’s path through the network.

FieldValue
text To understand MoE, we should first answer why we need such a system, especially in the context of LLMs? The scale of a model is one of the most important factors for judging the model quality. The mixture of Experts enables models to be pretrained with far less compute, which means we can dramatically scale up the model or dataset size with the same compute budget as a dense model. In particular, a MoE model should achieve the same quality as its dense counterpart much faster during pretraining.…
label r/deeplearning
dataType post
communityName r/deeplearning
datetime 2024-05-23
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Raw Record

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  "text": "To understand MoE, we should first answer why we need such a system, especially in the context of LLMs? The scale of a model is one of the most important factors for judging the model quality. The mixture of Experts enables models to be pretrained with far less compute, which means we can dramatically scale up the model or dataset size with the same compute budget as a dense model. In particular, a MoE model should achieve the same quality as its dense counterpart much faster during pretraining.\n\nLet’s understand the mechanism behind MOE.\n\n# Full article: [https://medium.com/aiguys/moe-and-llm-merging-recipes-741b11da2b4c](https://medium.com/aiguys/moe-and-llm-merging-recipes-741b11da2b4c)\n\nA standard transformer block contains the self-attention mechanism, followed by a feed-forward layer. Whereas in MoE, Sparse MoE layers are used instead of dense feed-forward network (FFN) layers. MoE layers have a certain number of “experts” (e.g. 8), where each expert is a neural network. In practice, the experts are FFNs, but they can also be more complex networks or even a MoE itself, leading to hierarchical MoEs!\n\n  \n**The experts help predict one token at a time**\n\n**The router decides which expert is active at each token**\n\n**Experts are combined through a gated linear combination to predict the next token**\n\n  \nAlthough MoEs provide benefits like efficient pretraining and faster inference compared to dense models, they also come with challenges:\n\n* **Training:** MoEs enable significantly more compute-efficient pretraining, but they’ve historically struggled to generalize during fine-tuning, leading to overfitting.\n* **Inference:** Although a MoE might have many parameters, only some of them are used during inference. This leads to much faster inference compared to a dense model with the same number of parameters.\n\n# Evolutionary Merging\n\n# Parameter (PS) merging\n\nThis type of merging focuses on combining the weights of multiple models to form a single, more powerful model while maintaining the original neural network architecture. This approach involves integrating the parameters (weights and biases) of the models layer by layer.\n\n# Data Flow Space (DFS) Merging\n\nData Flow Space (DFS) Merging optimizes the sequence in which data flows through the layers of different models. Unlike PS Merging, DFS Merging keeps the original weights intact and focuses on the data’s path through the network.\n\n  \n",
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