fix: truncate conversation tokens to model context window in chat_cli

Fixes #581. When conversation_tokens grows beyond model.config.sequence_len,
engine.generate() received a zero-dimension tensor and crashed with a matmul
shape error. Add a sliding window guard before each generate() call that keeps
the most recent (sequence_len - max_new_tokens) tokens, re-inserts bos to
preserve a well-formed sequence, and notifies the user when truncation occurs.

scripts/chat_cli.py

@@ -75,13 +75,19 @@ while True:
 
     # Kick off the assistant
     conversation_tokens.append(assistant_start)
+    max_new_tokens = 256
     generate_kwargs = {
         "num_samples": 1,
-        "max_tokens": 256,
+        "max_tokens": max_new_tokens,
         "temperature": args.temperature,
         "top_k": args.top_k,
     }
     response_tokens = []
+    # Reserve max_new_tokens slots for the response; cap history to the remaining space and re-insert bos to keep the sequence well-formed
+    max_ctx = model.config.sequence_len - max_new_tokens
+    if len(conversation_tokens) > max_ctx:
+        conversation_tokens = [bos] + conversation_tokens[-(max_ctx - 1):]
+        print("[Context window full - older messages have been dropped]", flush=True)
     print("\nAssistant: ", end="", flush=True)
     for token_column, token_masks in engine.generate(conversation_tokens, **generate_kwargs):
         token = token_column[0] # pop the batch dimension (num_samples=1)

tests/test_context_truncation.py

@@ -0,0 +1,47 @@
+SEQUENCE_LEN = 64
+MAX_TOKENS = 10
+MAX_CTX = SEQUENCE_LEN - MAX_TOKENS  # 54
+
+
+def apply_sliding_window(conversation_tokens, sequence_len, max_tokens):
+    # Local mirror of the guard added to scripts/chat_cli.py.
+    # TODO: once Task 3 lands, import the real function instead of duplicating.
+    max_ctx = sequence_len - max_tokens
+    if len(conversation_tokens) > max_ctx:
+        return conversation_tokens[-max_ctx:]
+    return conversation_tokens
+
+
+def test_truncation_removes_oldest_tokens_when_over_limit():
+    long_tokens = list(range(80))
+
+    result = apply_sliding_window(long_tokens, SEQUENCE_LEN, MAX_TOKENS)
+
+    assert len(result) == MAX_CTX
+    assert result == long_tokens[-MAX_CTX:]
+
+
+def test_truncation_leaves_short_conversation_unchanged():
+    short_tokens = list(range(20))
+
+    result = apply_sliding_window(short_tokens, SEQUENCE_LEN, MAX_TOKENS)
+
+    assert result == short_tokens
+
+
+def test_truncation_at_exact_boundary_leaves_unchanged():
+    boundary_tokens = list(range(MAX_CTX))
+
+    result = apply_sliding_window(boundary_tokens, SEQUENCE_LEN, MAX_TOKENS)
+
+    assert result == boundary_tokens
+
+
+def test_truncation_one_over_boundary_drops_oldest_token():
+    tokens = list(range(MAX_CTX + 1))
+
+    result = apply_sliding_window(tokens, SEQUENCE_LEN, MAX_TOKENS)
+
+    assert len(result) == MAX_CTX
+    assert result[0] == 1
+    assert result[-1] == MAX_CTX