Make chat output less verbose (#1123)

litgpt/chat/base.py

@@ -142,7 +142,6 @@ def main(
         print("Merging LoRA weights with the base model. This won't take long and is a one-time-only thing.")
         merge_lora(checkpoint_path)
 
-    fabric.print(f"Loading model {str(checkpoint_path)!r} with {config.__dict__}", file=sys.stderr)
     with fabric.init_module(empty_init=True):
         model = GPT(config)
         # enable the kv cache
@@ -163,13 +162,14 @@ def main(
     prompt_style = load_prompt_style(checkpoint_dir) if has_prompt_style(checkpoint_dir) else PromptStyle.from_config(config)
     stop_tokens = prompt_style.stop_tokens(tokenizer)
 
+    print(f"Now chatting with {config.name}.\nTo exit, press 'Enter' on an empty prompt.\n")
     L.seed_everything(1234)
     while True:
         try:
             prompt = input(">> Prompt: ")
         except KeyboardInterrupt:
             break
-        if not prompt:
+        if prompt.lower().strip() in ("", "quit", "exit"):
             break
         prompt = prompt_style.apply(prompt=prompt)
         encoded_prompt = tokenizer.encode(prompt, device=fabric.device)

tests/test_chat.py

@@ -1,5 +1,5 @@
 # Copyright Lightning AI. Licensed under the Apache License 2.0, see LICENSE file.
-
+import re
 import subprocess
 import sys
 from contextlib import redirect_stderr, redirect_stdout
@@ -87,7 +87,15 @@ def test_main(mocked_input, stop_iteration, fake_checkpoint_dir, monkeypatch, te
     mocked_input.side_effect = ["Hello", stop_iteration]
 
     config_path = fake_checkpoint_dir / "model_config.yaml"
-    config = {"block_size": 128, "vocab_size": 50, "n_layer": 2, "n_head": 4, "n_embd": 8, "rotary_percentage": 1}
+    config = {
+        "name": "Llama 3",
+        "block_size": 128,
+        "vocab_size": 50,
+        "n_layer": 2,
+        "n_head": 4,
+        "n_embd": 8,
+        "rotary_percentage": 1,
+    }
     config_path.write_text(yaml.dump(config))
 
     load_mock = Mock()
@@ -112,10 +120,8 @@ def test_main(mocked_input, stop_iteration, fake_checkpoint_dir, monkeypatch, te
     assert generate_mock.mock_calls == [
         call(ANY, tensor_like, 128, temperature=2.0, top_k=2, stop_tokens=([tokenizer_mock.return_value.eos_id],))
     ]
-    # # only the generated result is printed to stdout
-    assert out.getvalue() == ">> Reply: foo bar baz\n"
-
-    assert "'padded_vocab_size': 512, 'n_layer': 2, 'n_head': 4" in err.getvalue()
+    # only the generated result is printed to stdout
+    assert re.match("Now chatting with Llama 3.*>> .*Reply: foo bar baz", out.getvalue(), re.DOTALL)
 
 
 @pytest.mark.parametrize("mode", ["file", "entrypoint"])