Fixed vocab issues

.gitignore

@@ -173,3 +173,4 @@ cython_debug/
 /data/books/wizard_of_oz.txt
 /data/memory/context.json
 /data/memory/dreams.json
+/data/memory/vocab.json

model/brain.py

@@ -3,13 +3,13 @@ import torch
 import torch.nn.functional as F
 from model.memory import save_dream
 from model.brain_state import model, tokenizer, DEVICE
+from context.context import get_recent_context
 
 recent_dreams = []
 
 
 def generate_response():
     model.eval()
-    # Pick a real known word to seed from context memory
     context_texts = get_recent_context(5)
     if context_texts:
         start = random.choice(context_texts)
@@ -22,12 +22,13 @@ def generate_response():
         seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE).unsqueeze(0)
 
     output = model(seed)
-    pred = torch.argmax(output, dim=-1).squeeze().tolist()
+    pred = torch.argmax(output, dim=-1).squeeze().item()
 
-    if not isinstance(pred, list):
+    # Clamp prediction into known vocab range
-        pred = [pred]
+    if pred >= tokenizer.next_id:
+        pred = random.randint(0, tokenizer.next_id - 1)
 
-    return tokenizer.detokenize(pred)
+    return tokenizer.detokenize([pred])
 
 
 def score_sentence(sentence: str) -> float:

model/brain_state.py

@@ -6,7 +6,7 @@ from model.tokenizer import Tokenizer
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 tokenizer = Tokenizer()
-VOCAB_SIZE = 10000  # Expandable if needed
+VOCAB_SIZE = len(tokenizer.vocab) + 10  # Slight buffer
 
 model = TinyTransformer(vocab_size=VOCAB_SIZE).to(DEVICE)
 optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

model/dynamic_expand.py

@@ -0,0 +1,41 @@
+import torch
+from model.brain_architecture import TinyTransformer
+from model.brain_state import model, tokenizer, DEVICE, optimizer
+import copy
+
+
+def expand_model_if_needed():
+    current_vocab_size = len(tokenizer.vocab) + 10  # Tiny buffer
+    old_vocab_size = model.head.out_features
+
+    if current_vocab_size <= old_vocab_size:
+        return  # No expansion needed
+
+    print(f"Expanding model from {old_vocab_size} -> {current_vocab_size}")
+
+    # Save old model
+    old_model = copy.deepcopy(model).to('cpu')
+
+    # Create new model
+    new_model = TinyTransformer(vocab_size=current_vocab_size).to(DEVICE)
+    new_optimizer = torch.optim.Adam(new_model.parameters(), lr=1e-4)
+
+    # Copy parameters
+    with torch.no_grad():
+        for name, param in old_model.named_parameters():
+            if name in dict(new_model.named_parameters()):
+                try:
+                    new_param = dict(new_model.named_parameters())[name]
+                    if param.shape == new_param.shape:
+                        new_param.copy_(param)
+                    else:
+                        print(f"Skipping mismatched param: {name}")
+                except Exception as e:
+                    print(f"Error copying param: {name} — {e}")
+
+    # Replace global references
+    globals()["model"] = new_model
+    globals()["optimizer"] = new_optimizer
+
+    print("Expansion complete.")
+

model/trainer.py

@@ -2,6 +2,7 @@ import torch
 import time
 from model.brain_state import model, tokenizer, DEVICE, optimizer, loss_fn
 from context.context import add_to_context, get_recent_context
+from model.dynamic_expand import expand_model_if_needed
 
 LOSS_FILE = "data/logs/loss.log"
 
@@ -12,6 +13,7 @@ def log_loss(value: float):
 
 
 def train_on_message(text: str):
+    expand_model_if_needed()
     model.train()
     context_texts = get_recent_context(3)
     augmented_text = " ".join(context_texts + [text])