forked from phoenix-oss/llama-stack-mirror
4d0bfbf984
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.
70 lines
2.2 KiB
Python
70 lines
2.2 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 llama_stack.cli.subcommand import Subcommand
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from llama_stack.cli.table import print_table
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from llama_stack.models.llama.sku_list import resolve_model
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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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required=True,
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help="See `llama model list` or `llama model list --show-all` for the list of available models",
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)
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def _run_model_describe_cmd(self, args: argparse.Namespace) -> None:
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from .safety_models import prompt_guard_model_sku_map
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prompt_guard_model_map = prompt_guard_model_sku_map()
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if args.model_id in prompt_guard_model_map.keys():
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model = prompt_guard_model_map[args.model_id]
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else:
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model = resolve_model(args.model_id)
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if model is None:
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self.parser.error(
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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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headers = [
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"Model",
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model.descriptor(),
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]
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rows = [
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("Hugging Face ID", model.huggingface_repo or "<Not Available>"),
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("Description", model.description),
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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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("Model params.json", json.dumps(model.arch_args, indent=4)),
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]
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print_table(
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rows,
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headers,
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separate_rows=True,
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)
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