llama-stack-mirror/llama_stack/cli/model/describe.py
Ashwin Bharambe 4d0bfbf984
feat: add api.llama provider, llama-guard-4 model (#2058)
This PR adds a llama-stack inference provider for `api.llama.com`, as
well as adds entries for Llama-Guard-4 and updated Prompt-Guard models.
2025-04-29 10:07:41 -07:00

70 lines
2.2 KiB
Python

# 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_stack.cli.subcommand import Subcommand
from llama_stack.cli.table import print_table
from llama_stack.models.llama.sku_list import resolve_model
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,
help="See `llama model list` or `llama model list --show-all` for the list of available models",
)
def _run_model_describe_cmd(self, args: argparse.Namespace) -> None:
from .safety_models import prompt_guard_model_sku_map
prompt_guard_model_map = prompt_guard_model_sku_map()
if args.model_id in prompt_guard_model_map.keys():
model = prompt_guard_model_map[args.model_id]
else:
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
headers = [
"Model",
model.descriptor(),
]
rows = [
("Hugging Face ID", model.huggingface_repo or "<Not Available>"),
("Description", model.description),
("Context Length", f"{model.max_seq_length // 1024}K tokens"),
("Weights format", model.quantization_format.value),
("Model params.json", json.dumps(model.arch_args, indent=4)),
]
print_table(
rows,
headers,
separate_rows=True,
)