Problem Summary

Fixing the issue with DeepSeek-V3.2 model inference

Root Cause Analysis

The issue is likely related to the model architecture or configuration.

Fix Plan

Update Model Configuration

1. Check the model configuration file (model.py) for any potential issues.
2. Ensure that the model architecture is correctly defined and matches the expected behavior.
3. Verify that the input and output shapes are correctly configured.

Update Model Code

1. Update the inference function in model.py to correctly handle input data.
2. Use the transformers library to load the pre-trained model and fine-tune it if necessary.
3. Use the vllm library to load the VLLM model and integrate it with the DeepSeek-V3.2 model.

Example Code

import torch
from transformers import GLMForSequenceClassification
from vllm import VLLM

class DeepSeekV3_2Model(torch.nn.Module):
    def __init__(self):
        super(DeepSeekV3_2Model, self).__init__()
        self.glm = GLMForSequenceClassification.from_pretrained('glm-moe-dsa')
        self.vllm = VLLM.from_pretrained('vllm-model')

    def forward(self, input_ids, attention_mask):
        # Run the GLM model
        glm_output = self.glm(input_ids, attention_mask)
        
        # Run the VLLM model
        vllm_output = self.vllm(input_ids, attention_mask)
        
        # Combine the outputs
        output = torch.cat((glm_output, vllm_output), dim=1)
        
        return output

Verification

1. Run the updated model on a test dataset to verify that it produces the expected output.
2. Compare the output with the expected behavior to ensure that the fix is successful.

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