forked from phoenix-oss/llama-stack-mirror
122 lines
4.2 KiB
Python
122 lines
4.2 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 textwrap
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from io import StringIO
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from pathlib import Path
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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.datatypes import CoreModelId, ModelFamily, is_multimodal, model_family
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ROOT_DIR = Path(__file__).parent.parent
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class ModelPromptFormat(Subcommand):
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"""Llama model cli for describe a model prompt format (message formats)"""
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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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"prompt-format",
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prog="llama model prompt-format",
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description="Show llama model message formats",
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epilog=textwrap.dedent(
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"""
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Example:
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llama model prompt-format <options>
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"""
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),
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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_template_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-name",
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type=str,
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default="llama3_1",
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help="Model Family (llama3_1, llama3_X, etc.)",
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)
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def _run_model_template_cmd(self, args: argparse.Namespace) -> None:
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import importlib.resources
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# Only Llama 3.1 and 3.2 are supported
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supported_model_ids = [
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m for m in CoreModelId if model_family(m) in {ModelFamily.llama3_1, ModelFamily.llama3_2}
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]
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model_list = [m.value for m in supported_model_ids]
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model_str = "\n".join(model_list)
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if args.list:
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headers = ["Model(s)"]
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rows = []
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for m in model_list:
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rows.append(
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[
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m,
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]
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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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return
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try:
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model_id = CoreModelId(args.model_name)
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except ValueError:
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self.parser.error(f"{args.model_name} is not a valid Model. Choose one from --\n{model_str}")
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if model_id not in supported_model_ids:
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self.parser.error(f"{model_id} is not a valid Model. Choose one from --\n {model_str}")
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llama_3_1_file = ROOT_DIR / "models" / "llama" / "llama3_1" / "prompt_format.md"
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llama_3_2_text_file = ROOT_DIR / "models" / "llama" / "llama3_2" / "text_prompt_format.md"
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llama_3_2_vision_file = ROOT_DIR / "models" / "llama" / "llama3_2" / "vision_prompt_format.md"
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if model_family(model_id) == ModelFamily.llama3_1:
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with importlib.resources.as_file(llama_3_1_file) as f:
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content = f.open("r").read()
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elif model_family(model_id) == ModelFamily.llama3_2:
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if is_multimodal(model_id):
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with importlib.resources.as_file(llama_3_2_vision_file) as f:
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content = f.open("r").read()
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else:
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with importlib.resources.as_file(llama_3_2_text_file) as f:
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content = f.open("r").read()
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render_markdown_to_pager(content)
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def render_markdown_to_pager(markdown_content: str):
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from rich.console import Console
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from rich.markdown import Markdown
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from rich.style import Style
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from rich.text import Text
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class LeftAlignedHeaderMarkdown(Markdown):
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def parse_header(self, token):
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level = token.type.count("h")
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content = Text(token.content)
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header_style = Style(color="bright_blue", bold=True)
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header = Text(f"{'#' * level} ", style=header_style) + content
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self.add_text(header)
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# Render the Markdown
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md = LeftAlignedHeaderMarkdown(markdown_content)
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# Capture the rendered output
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output = StringIO()
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console = Console(file=output, force_terminal=True, width=100) # Set a fixed width
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console.print(md)
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rendered_content = output.getvalue()
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print(rendered_content)
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