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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+

model.safetensors

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 version https://git-lfs.github.com/spec/v1
-oid sha256:54ea39c58da645f5e05e755cefa42896357814d2b3c643b749685339a6e785ce
-size 2372917736
+oid sha256:7536ba05641384b6e8add6bc4ede22069e249f8b0716b1045a4b841a74cbb4b8
+size 2991581672

modeling_moe.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PreTrainedModel, AutoModelForImageClassification
+from .configuration_moe import MoEConfig
+
+
+def subgate(num_classes):
+    layers = nn.Sequential(
+        nn.Flatten(),
+        nn.Linear(224 * 224 * 3, 1024),
+        nn.ReLU(),
+        nn.Linear(1024, 512),
+        nn.ReLU(),
+        nn.Linear(512, num_classes * 2),
+    )
+    return layers
+
+
+class MoEModelForImageClassification(PreTrainedModel):
+    config_class = MoEConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_classes = config.num_classes
+        self.switch_gate_model = AutoModelForImageClassification.from_pretrained(
+            config.switch_gate
+        )
+        self.base_model1 = AutoModelForImageClassification.from_pretrained(
+            config.base_model
+        )
+        self.base_model2 = AutoModelForImageClassification.from_pretrained(
+            config.base_model
+        )
+        self.expert_model_1 = AutoModelForImageClassification.from_pretrained(
+            config.experts[0]
+        )
+        self.expert_model_2 = AutoModelForImageClassification.from_pretrained(
+            config.experts[1]
+        )
+
+        self.subgate1 = subgate(config.num_classes)
+        self.subgate2 = subgate(config.num_classes)
+
+        # Freeze all params
+        for module in [
+            self.base_model1,
+            self.base_model2,
+            self.expert_model_1,
+            self.expert_model_2,
+        ]:
+            for param in module.parameters():
+                param.requires_grad = False
+
+    def forward(self, pixel_values, labels=None):
+        switch_gate_result = self.switch_gate_model(pixel_values).logits
+        base_model1_result = self.base_model1(pixel_values).logits
+        base_model2_result = self.base_model2(pixel_values).logits
+
+        expert1_result = self.expert_model_1(pixel_values).logits
+        expert2_result = self.expert_model_2(pixel_values).logits
+
+        subgate1_result = self.subgate1(pixel_values)
+        subgate1_result = torch.reshape(subgate1_result, (2, -1, self.num_classes))
+
+        subgate2_result = self.subgate2(pixel_values)
+        subgate2_result = torch.reshape(subgate2_result, (2, -1, self.num_classes))
+
+        expert1_and_base_res = (
+            expert1_result * subgate1_result[0, :, :]
+            + base_model1_result * subgate1_result[1, :, :]
+        )
+        expert2_and_base_res = (
+            expert2_result * subgate2_result[0, :, :]
+            + base_model2_result * subgate2_result[1, :, :]
+        )
+
+        # Gating Network
+        expert1_and_base_res = expert1_and_base_res * switch_gate_result[
+            :, 0
+        ].unsqueeze(1)
+        expert2_and_base_res = expert2_and_base_res * switch_gate_result[
+            :, 1
+        ].unsqueeze(1)
+
+        logits = expert1_and_base_res + expert2_and_base_res
+        if labels is not None:
+            loss = F.cross_entropy(logits, labels)
+            return {"loss": loss, "logits": logits}
+        return {"logits": logits}