Fixing the CUDA errors

Fixing replies.

model/brain.py

@@ -10,28 +10,43 @@ from context.context import get_recent_context
 recent_dreams = []
 
 
+@torch.no_grad()
 def generate_response():
     model.eval()
-    context_texts = get_recent_context(5)
+    context_texts = get_recent_context(10)
-    if context_texts:
+    seed_text = " ".join(context_texts[-1:])
-        start = random.choice(context_texts)
+    tokens = tokenizer.tokenize(seed_text)
-        seed_tokens = tokenizer.tokenize(start)
-        if seed_tokens:
-            seed = torch.tensor([seed_tokens[-1]], device=DEVICE).unsqueeze(0)
-            seed = seed[:, -128:]
-        else:
-            seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE).unsqueeze(0)
-    else:
-        seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE).unsqueeze(0)
 
-    output = model(seed)
+    input_tensor = torch.tensor(tokens, dtype=torch.long, device=DEVICE).unsqueeze(0)
-    pred = torch.argmax(output, dim=-1).squeeze().item()
 
-    # Clamp prediction into known vocab range
+    output_tokens = []
-    if pred >= tokenizer.next_id:
+    max_tokens = 32
-        pred = random.randint(0, tokenizer.next_id - 1)
 
-    return tokenizer.detokenize([pred])
+    for _ in range(max_tokens):
+        output = model(input_tensor)
+        logits = output[:, -1, :].squeeze(0)
+
+        # Apply temperature (soft randomness)
+        temperature = 0.8
+        logits = logits / temperature
+
+        # Top-k sampling
+        k = 10
+        topk_logits, topk_indices = torch.topk(logits, k)
+        probs = torch.nn.functional.softmax(topk_logits, dim=-1)
+        next_token = topk_indices[torch.multinomial(probs, 1)].item()
+
+        output_tokens.append(next_token)
+
+        input_tensor = torch.cat([input_tensor, torch.tensor([[next_token]], device=DEVICE)], dim=1)
+
+        # Optional: stop if next_token maps to period, question mark, or exclamation
+        next_char = tokenizer.detokenize([next_token])
+        if any(p in next_char for p in [".", "?", "!"]):
+            break
+
+    text = tokenizer.detokenize(output_tokens)
+    return text
 
 
 def score_sentence(sentence: str) -> float:

model/dynamic_expand.py

@@ -28,7 +28,12 @@ def expand_model_if_needed():
         # print(f"[Expand] Expanding model from {old_vocab_size} -> {current_vocab_size}")
 
         old_state = model.state_dict()
-        new_model = TinyTransformer(vocab_size=current_vocab_size).to(DEVICE)
+        new_model = TinyTransformer(
+            vocab_size=current_vocab_size,
+            embed_dim=model.token_embed.embedding_dim,
+            depth=len(model.blocks),
+            heads=model.blocks[0].attn.heads
+        ).to(DEVICE)
 
         with torch.no_grad():
             for name, param in new_model.named_parameters():