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