Update evo_model.py

evo_model.py

@@ -4,20 +4,21 @@ import torch.nn as nn
 import math
 
 class PositionalEncoding(nn.Module):
-    def __init__(self, d_model, max_len=512):  # ⬅ increased to 512
+    def __init__(self, d_model, max_len=128):
         super().__init__()
         pe = torch.zeros(max_len, d_model)
         position = torch.arange(0, max_len).unsqueeze(1)
         div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
         pe[:, 0::2] = torch.sin(position * div_term)
         pe[:, 1::2] = torch.cos(position * div_term)
-        pe = pe.unsqueeze(0)  # shape: (1, max_len, d_model)
+        pe = pe.unsqueeze(0)  # (1, max_len, d_model)
         self.register_buffer('pe', pe)
+        self.max_len = max_len
 
     def forward(self, x):
         seq_len = x.size(1)
-        if seq_len > self.pe.size(1):
-            raise ValueError(f"Input length {seq_len} exceeds max_len {self.pe.size(1)}")
+        if seq_len > self.max_len:
+            raise ValueError(f"Input length {seq_len} exceeds max_len {self.max_len}")
         return x + self.pe[:, :seq_len]
 
 class EvoDecoderModel(nn.Module):
@@ -25,18 +26,11 @@ class EvoDecoderModel(nn.Module):
         super().__init__()
         self.token_embed = nn.Embedding(vocab_size, d_model)
         self.pos_encoder = PositionalEncoding(d_model)
-        decoder_layer = nn.TransformerDecoderLayer(
-            d_model=d_model,
-            nhead=nhead,
-            dim_feedforward=dim_feedforward,
-            dropout=dropout,
-            batch_first=True
-        )
+        decoder_layer = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, batch_first=True)
         self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)
         self.lm_head = nn.Linear(d_model, vocab_size)
 
     def forward(self, input_ids):
-        input_ids = input_ids[:, :128]  # ⬅ clip input to match saved model's trained length
         x = self.token_embed(input_ids)
         x = self.pos_encoder(x)
         seq_len = x.size(1)