File size: 1,468 Bytes
2e1a430 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | import torch
class AceStepTextEncoder(torch.nn.Module):
def __init__(
self,
):
super().__init__()
from transformers import Qwen3Config, Qwen3Model
config = Qwen3Config(
attention_bias=False,
attention_dropout=0.0,
bos_token_id=151643,
dtype="bfloat16",
eos_token_id=151643,
head_dim=128,
hidden_act="silu",
hidden_size=1024,
initializer_range=0.02,
intermediate_size=3072,
layer_types=["full_attention"] * 28,
max_position_embeddings=32768,
max_window_layers=28,
model_type="qwen3",
num_attention_heads=16,
num_hidden_layers=28,
num_key_value_heads=8,
pad_token_id=151643,
rms_norm_eps=1e-06,
rope_scaling=None,
rope_theta=1000000,
sliding_window=None,
tie_word_embeddings=True,
use_cache=True,
use_sliding_window=False,
vocab_size=151669,
)
self.model = Qwen3Model(config)
@torch.no_grad()
def forward(
self,
input_ids: torch.LongTensor,
attention_mask: torch.Tensor,
):
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=True,
)
return outputs.last_hidden_state
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