diff --git a/ch06/01_main-chapter-code/previous_chapters.py b/ch06/01_main-chapter-code/previous_chapters.py
index e794f9b..59a5017 100644
--- a/ch06/01_main-chapter-code/previous_chapters.py
+++ b/ch06/01_main-chapter-code/previous_chapters.py
@@ -310,36 +310,12 @@ def load_weights_into_gpt(gpt, params):
     gpt.out_head.weight = assign(gpt.out_head.weight, params["wte"])
 
 
-def generate(model, idx, max_new_tokens, context_size, temperature, top_k=None):
-    # For-loop is the same as before: Get logits, and only focus on last time step
-    for _ in range(max_new_tokens):
-        idx_cond = idx[:, -context_size:]
-        with torch.no_grad():
-            logits = model(idx_cond)
-        logits = logits[:, -1, :]
+def text_to_token_ids(text, tokenizer):
+    encoded = tokenizer.encode(text, allowed_special={'<|endoftext|>'})
+    encoded_tensor = torch.tensor(encoded).unsqueeze(0) # add batch dimension
+    return encoded_tensor
 
-        # New: Filter logits with top_k sampling
-        if top_k is not None:
-            # Keep only top_k values
-            top_logits, _ = torch.topk(logits, top_k)
-            min_val = top_logits[:, -1]
-            logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits)
 
-        # New: Apply temperature scaling
-        if temperature > 0.0:
-            logits = logits / temperature
-
-            # Apply softmax to get probabilities
-            probs = torch.softmax(logits, dim=-1)  # (batch_size, context_len)
-
-            # Sample from the distribution
-            idx_next = torch.multinomial(probs, num_samples=1)  # (batch_size, 1)
-
-        # Otherwise same as before: get idx of the vocab entry with the highest logits value
-        else:
-            idx_next = torch.argmax(logits, dim=-1, keepdim=True)  # (batch_size, 1)
-
-        # Same as before: append sampled index to the running sequence
-        idx = torch.cat((idx, idx_next), dim=1)  # (batch_size, num_tokens+1)
-
-    return idx
+def token_ids_to_text(token_ids, tokenizer):
+    flat = token_ids.squeeze(0) # remove batch dimension
+    return tokenizer.decode(flat.tolist())