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add model (#155)
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{
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"framework": "torch",
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"num_devices_required": 1,
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"num_nodes_required": 1,
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"dynamic": false
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}

samples/transformers-auto-model/UAE-Large-V1/input_meta.py

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samples/transformers-auto-model/UAE-Large-V1/input_tensor_constraints.py

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import torch
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class GraphModule(torch.nn.Module):
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def forward(self, L_features_token_embeddings_: torch.Tensor):
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l_features_token_embeddings_ = L_features_token_embeddings_
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cls_token = l_features_token_embeddings_[(slice(None, None, None), 0)]
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l_features_token_embeddings_ = None
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output_vector = torch.cat([cls_token], 1)
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cls_token = None
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return (output_vector,)
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class Program_weight_tensor_meta_L_features_token_embeddings_:
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name = "L_features_token_embeddings_"
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shape = [1, 10, 1024]
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dtype = "torch.float32"
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device = "cpu"
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mean = -0.000
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std = 0.649
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data = None
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aa1886598b55df1dbc5d2f8e8114ecdb0a592167f14e345f19fd23fb630fb3aa
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{
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"framework": "torch",
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"num_devices_required": 1,
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"num_nodes_required": 1,
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"dynamic": false
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}

samples/transformers-auto-model/bge-base-en-v1.5/input_meta.py

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samples/transformers-auto-model/bge-base-en-v1.5/input_tensor_constraints.py

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