llama_toolchain -> llama_stack

llama_stack/cli/model/describe.py

@@ -0,0 +1,75 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import argparse
+import json
+
+from llama_models.sku_list import resolve_model
+
+from termcolor import colored
+
+from llama_stack.cli.subcommand import Subcommand
+from llama_stack.cli.table import print_table
+from llama_stack.common.serialize import EnumEncoder
+
+
+class ModelDescribe(Subcommand):
+    """Show details about a model"""
+
+    def __init__(self, subparsers: argparse._SubParsersAction):
+        super().__init__()
+        self.parser = subparsers.add_parser(
+            "describe",
+            prog="llama model describe",
+            description="Show details about a llama model",
+            formatter_class=argparse.RawTextHelpFormatter,
+        )
+        self._add_arguments()
+        self.parser.set_defaults(func=self._run_model_describe_cmd)
+
+    def _add_arguments(self):
+        self.parser.add_argument(
+            "-m",
+            "--model-id",
+            type=str,
+            required=True,
+        )
+
+    def _run_model_describe_cmd(self, args: argparse.Namespace) -> None:
+        model = resolve_model(args.model_id)
+        if model is None:
+            self.parser.error(
+                f"Model {args.model_id} not found; try 'llama model list' for a list of available models."
+            )
+            return
+
+        rows = [
+            (
+                colored("Model", "white", attrs=["bold"]),
+                colored(model.descriptor(), "white", attrs=["bold"]),
+            ),
+            ("HuggingFace ID", model.huggingface_repo or "<Not Available>"),
+            ("Description", model.description_markdown),
+            ("Context Length", f"{model.max_seq_length // 1024}K tokens"),
+            ("Weights format", model.quantization_format.value),
+            ("Model params.json", json.dumps(model.model_args, indent=4)),
+        ]
+
+        if model.recommended_sampling_params is not None:
+            sampling_params = model.recommended_sampling_params.dict()
+            for k in ("max_tokens", "repetition_penalty"):
+                del sampling_params[k]
+            rows.append(
+                (
+                    "Recommended sampling params",
+                    json.dumps(sampling_params, cls=EnumEncoder, indent=4),
+                )
+            )
+
+        print_table(
+            rows,
+            separate_rows=True,
+        )