初始化项目，由ModelHub XC社区提供模型

Model: kalomaze/Mistral-7b-MoEified-8x
Source: Original Platform

README.md

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+---
+license: apache-2.0
+---
+# Wtf is a MoEification?!
+Turns out, you can slice up the individual MLP layers of a dense language model into even splits of experts.
+
+What I did here:
+- Split the MLP projections (gate, down, proj) into the amount of total experts you want (in this case, I just went with 8 experts).
+- Multiply the values of the parameters for the down-projection by the total amount of experts (so the magnitude of the activation outputs, when averaged linearly together, ends up being equivalent)
+- Initialize the router layers with zeroes, so the expert usage is completely equal by default and has no unintentional biases as a consequence of random initialization being done the normal way.
+
+As a result, the model behaves completely coherently when all 8 experts are activated (i.e, experts_per_tok is equal to 8.)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6491e00e057b0928b3e07b75/iudC2DgvaErI_rwe2Vxjf.png)
+
+With 4 experts activated, it's... far less coherent.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6491e00e057b0928b3e07b75/l1-10MRdprnM4WrtMAbcm.png)
+
+# Ok but why?
+
+I am interested in the prospect of continuing to train this in such a way where it can naturally handle variable expert counts, and learn to balance the features.
+If this works, we can potentially teach the behavior of using less computation for tokens that are trivial to predict, while using more when necessary.
+
+# Also thanks StefanGliga for giving me the idea while we were discussing [this paper](https://arxiv.org/abs/2303.01610) :3