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fb0011a | 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 | import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
class GemmaWrapper(nn.Module):
def __init__(self, model_name="google/gemma-3-4b-it", device=None):
super().__init__()
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModel.from_pretrained(model_name, attn_implementation='eager').to(device)
self.model.eval()
for param in self.model.parameters():
param.requires_grad = False
def forward(self, texts):
inputs = self.tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=512).to(self.model.device)
with torch.no_grad():
outputs = self.model(**inputs)
last_hidden_state = outputs.last_hidden_state
attention_mask = inputs.attention_mask
return last_hidden_state, attention_mask.bool()
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