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2aed081 | 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 | import torch
import torch.nn as nn
from transformers import AutoModel
from peft import get_peft_model, LoraConfig, TaskType
class VanillaBaselineModel(nn.Module):
"""
A simple baseline deep learning model that takes the entire trajectory
and predicts if a hallucination occurred.
It does NOT use the AgentSight Context Encoder or Dual Heads.
"""
def __init__(self, encoder_name="microsoft/deberta-v3-base"):
super().__init__()
base_encoder = AutoModel.from_pretrained(encoder_name, torch_dtype=torch.float32)
base_encoder.gradient_checkpointing_enable()
# Apply LoRA to the base encoder
peft_config = LoraConfig(
task_type=TaskType.FEATURE_EXTRACTION,
r=8,
lora_alpha=32,
lora_dropout=0.1,
target_modules=["query_proj", "value_proj"]
)
self.encoder = get_peft_model(base_encoder, peft_config)
self.encoder.print_trainable_parameters()
enc_dim = self.encoder.config.hidden_size
# Simple binary classification head
self.classifier = nn.Sequential(
nn.Linear(enc_dim, 64),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(64, 1) # Raw logits for BCEWithLogitsLoss
)
def forward(self, input_ids, attention_mask):
# Forward pass through DeBERTa (N_steps, max_len)
outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
# Use the representation of the [CLS] token (the first token) for each step
cls_repr = outputs.last_hidden_state[:, 0, :] # Shape: (N_steps, enc_dim)
# MEAN POOLING across all steps to create a single Trajectory Representation
# This guarantees gradients flow backwards through ALL steps, unlike max() pooling!
traj_repr = cls_repr.mean(dim=0).unsqueeze(0) # Shape: (1, enc_dim)
# Predict hallucination for the entire sequence
logits = self.classifier(traj_repr).squeeze(-1) # Shape: (1,)
return logits
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