| 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() | |