mirror of
https://github.com/meta-llama/llama-stack.git
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82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import argparse
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import json
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from enum import Enum
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from llama_models.llama3_1.api.sku_list import llama3_1_model_list
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from termcolor import colored
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from llama_toolchain.cli.subcommand import Subcommand
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from llama_toolchain.cli.table import print_table
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class EnumEncoder(json.JSONEncoder):
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def default(self, obj):
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if isinstance(obj, Enum):
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return obj.value
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return super().default(obj)
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class ModelDescribe(Subcommand):
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"""Show details about a model"""
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def __init__(self, subparsers: argparse._SubParsersAction):
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super().__init__()
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self.parser = subparsers.add_parser(
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"describe",
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prog="llama model describe",
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description="Show details about a llama model",
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formatter_class=argparse.RawTextHelpFormatter,
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)
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self._add_arguments()
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self.parser.set_defaults(func=self._run_model_describe_cmd)
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def _add_arguments(self):
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self.parser.add_argument(
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"-m",
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"--model-id",
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type=str,
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)
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def _run_model_describe_cmd(self, args: argparse.Namespace) -> None:
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models = llama3_1_model_list()
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by_id = {model.sku.value: model for model in models}
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if args.model_id not in by_id:
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print(
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f"Model {args.model_id} not found; try 'llama model list' for a list of available models."
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)
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return
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model = by_id[args.model_id]
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sampling_params = model.recommended_sampling_params.dict()
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for k in ("max_tokens", "repetition_penalty"):
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del sampling_params[k]
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rows = [
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(
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colored("Model", "white", attrs=["bold"]),
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colored(model.sku.value, "white", attrs=["bold"]),
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),
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("HuggingFace ID", model.huggingface_id or "<Not Available>"),
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("Description", model.description_markdown),
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("Context Length", f"{model.max_seq_length // 1024}K tokens"),
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("Weights format", model.quantization_format.value),
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(
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"Recommended sampling params",
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json.dumps(sampling_params, cls=EnumEncoder, indent=4),
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),
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("Model params.json", json.dumps(model.model_args, indent=4)),
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]
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print_table(
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rows,
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separate_rows=True,
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)
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