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():

model/reweaver.py

@@ -1,4 +1,5 @@
 import random
+import asyncio
 from context.context import load_context
 from model.trainer import train_on_message
 from model.dynamic_expand import expand_model_if_needed

model/trainer.py

@@ -23,7 +23,7 @@ def train_on_message(text: str, source: str = "user"):
     expand_model_if_needed()
 
     now = time.time()
-    if now - _last_expansion_time < 5:  # If expansion happened within the last 5 seconds
+    if now - _last_expansion_time < 5:
         print("[Train] Skipping to stabilize after expansion.")
         return
 
@@ -35,21 +35,32 @@ def train_on_message(text: str, source: str = "user"):
         model.train()
         context_texts = get_recent_context(10)
         augmented_text = " ".join(context_texts + [text])
+
         tokens = tokenizer.tokenize(augmented_text)
 
-        if len(tokens) < 2:
+        if not tokens or len(tokens) < 2:
             return
 
         max_token_id = model.head.out_features - 1
-        tokens = [min(t, max_token_id) for t in tokens]
 
-        if len(tokens) < 2:
+        # Clamp each token to be inside model's head size
+        clamped_tokens = []
+        for token in tokens:
+            if token > max_token_id:
+                clamped_tokens.append(max_token_id)
+            elif token < 0:
+                clamped_tokens.append(0)
+            else:
+                clamped_tokens.append(token)
+
+        # Clamp sequence length
+        clamped_tokens = clamped_tokens[:128]
+
+        if len(clamped_tokens) < 2:
             return
 
-        tokens = tokens[:128]
+        input_tensor = torch.tensor(clamped_tokens[:-1], dtype=torch.long, device=DEVICE).unsqueeze(0)
-
+        target_tensor = torch.tensor(clamped_tokens[1:], dtype=torch.long, device=DEVICE).unsqueeze(0)
-        input_tensor = torch.tensor(tokens[:-1], dtype=torch.long, device=DEVICE).unsqueeze(0)
-        target_tensor = torch.tensor(tokens[1:], dtype=torch.long, device=DEVICE).unsqueeze(0)
 
         opt = get_optimizer()
 
@@ -63,5 +74,9 @@ def train_on_message(text: str, source: str = "user"):
         log_loss(loss.item())
         log_vocab_growth()
         add_to_context(text, source=source)
+
+    except Exception as e:
+        print(f"[Train] Exception during training: {e}")
+
     finally:
         expand_lock.release()