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42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
from transformers import Qwen3Model, Qwen3Config
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import torch
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class ZImageTextEncoder(torch.nn.Module):
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def __init__(self):
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super().__init__()
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config = Qwen3Config(**{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": False,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 9728,
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"max_position_embeddings": 40960,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
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"rope_scaling": None,
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"rope_theta": 1000000,
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"sliding_window": None,
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"tie_word_embeddings": True,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.0",
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"use_cache": True,
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"use_sliding_window": False,
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"vocab_size": 151936
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})
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self.model = Qwen3Model(config)
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def forward(self, *args, **kwargs):
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return self.model(*args, **kwargs)
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