Support model name as a positional parameter

cli.py

@@ -207,7 +207,7 @@ def _add_arguments_common(parser):
     )
     parser.add_argument(
         '--model-directory',
-        type=str,
+        type=Path,
         default='.model-artifacts',
         help='The directory to store downloaded model artifacts'
     )

download.py

@@ -6,46 +6,57 @@
 import os
 
 from build.convert_hf_checkpoint import convert_hf_checkpoint
+from build.model import model_aliases
 from pathlib import Path
 from typing import Optional
 
 from requests.exceptions import HTTPError
 
-
-def hf_download(
-        repo_id: Optional[str] = None, 
-        model_dir: Optional[Path] = None,
+def download_and_convert(
+        model: str,
+        models_dir: Path,
         hf_token: Optional[str] = None) -> None:
     from huggingface_hub import snapshot_download
 
-    if model_dir is None:
-        model_dir = Path(".model-artifacts/{repo_id}")
+    if model in model_aliases:
+        model = model_aliases[model]
+
+    model_dir = models_dir / model
+    os.makedirs(model_dir, exist_ok=True)
 
+    # Download and store the HF model artifacts.
+    print(f"Downloading {model} from HuggingFace...")
     try:
         snapshot_download(
-            repo_id,
+            model,
             local_dir=model_dir,
             local_dir_use_symlinks=False,
             token=hf_token,
             ignore_patterns="*safetensors*")
     except HTTPError as e:
         if e.response.status_code == 401:
-            print("You need to pass a valid `--hf_token=...` to download private checkpoints.")
+            raise RuntimeError("You need to pass a valid `--hf_token=...` to download private checkpoints.")
         else:
             raise e
 
-
-def main(args):
-    model_dir = Path(args.model_directory) / args.model
-    os.makedirs(model_dir, exist_ok=True)
-
-    # Download and store the HF model artifacts.
-    print(f"Downloading {args.model} from HuggingFace...")
-    hf_download(args.model, model_dir, args.hf_token)
-
     # Convert the model to the torchchat format.
-    print(f"Converting {args.model} to torchchat format...")
+    print(f"Converting {model} to torchchat format...")
     convert_hf_checkpoint(
         model_dir=model_dir,
-        model_name=Path(args.model),
+        model_name=Path(model),
         remove_bin_files=True)
+
+def is_model_downloaded(
+        model: str, 
+        models_dir: Path) -> bool:
+    if model in model_aliases:
+        model = model_aliases[model]
+
+    model_dir = models_dir / model
+
+    # TODO Can we be more thorough here?
+    return os.path.isdir(model_dir)
+
+
+def main(args):
+    download_and_convert(args.model, args.model_directory, args.hf_token)

eval.py

@@ -20,6 +20,7 @@
 
 from build.model import Transformer
 from cli import add_arguments_for_eval, arg_init
+from download import download_and_convert, is_model_downloaded
 from generate import encode_tokens, model_forward
 
 from quantize import set_precision
@@ -221,6 +222,10 @@ def main(args) -> None:
 
     """
 
+    # If a named model was provided and not downloaded, download it.
+    if args.model and not is_model_downloaded(args.model, args.model_directory):
+        download_and_convert(args.model, args.model_directory, args.hf_token)
+
     builder_args = BuilderArgs.from_args(args)
     tokenizer_args = TokenizerArgs.from_args(args)
     quantize = args.quantize

export.py

@@ -11,6 +11,7 @@
 
 from build.builder import _initialize_model, BuilderArgs
 from cli import add_arguments_for_export, arg_init, check_args
+from download import download_and_convert, is_model_downloaded
 from export_aoti import export_model as export_model_aoti
 
 from quantize import set_precision
@@ -36,6 +37,10 @@ def device_sync(device):
 
 
 def main(args):
+    # If a named model was provided and not downloaded, download it.
+    if args.model and not is_model_downloaded(args.model, args.model_directory):
+        download_and_convert(args.model, args.model_directory, args.hf_token)
+
     builder_args = BuilderArgs.from_args(args)
     quantize = args.quantize
 

generate.py

@@ -25,6 +25,7 @@
 )
 from build.model import Transformer
 from cli import add_arguments_for_generate, arg_init, check_args
+from download import download_and_convert, is_model_downloaded
 from quantize import set_precision
 
 
@@ -402,6 +403,11 @@ def _main(
         device_sync(device=builder_args.device)
         if i >= 0 and generator_args.chat_mode:
             prompt = input("What is your prompt? ")
+
+            # DEBUG DO NOT COMMIT
+            B_INST = ""
+            E_INST = ""
+
             if chat_mode:
                 prompt = f"{B_INST} {prompt.strip()} {E_INST}"
             encoded = encode_tokens(
@@ -487,6 +493,11 @@ def callback(x):
 
 def main(args):
     is_chat = args.subcommand == "chat"
+
+    # If a named model was provided and not downloaded, download it.
+    if args.model and not is_model_downloaded(args.model, args.model_directory):
+        download_and_convert(args.model, args.model_directory, args.hf_token)
+
     builder_args = BuilderArgs.from_args(args)
     speculative_builder_args = BuilderArgs.from_speculative_args(args)
     tokenizer_args = TokenizerArgs.from_args(args)

torchchat.py

@@ -63,4 +63,4 @@
 
         export_main(args)
     else:
-        raise RuntimeError("Must specify valid subcommands: generate, export, eval")
+        raise RuntimeError("Must specify a valid subcommand: download, chat, generate, export, or eval.")