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https://github.com/meta-llama/llama-stack.git
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9e6561a1ec
# What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
64 lines
2.1 KiB
Python
64 lines
2.1 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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from typing import Any
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from pydantic import BaseModel, ConfigDict, Field
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from llama_stack.models.llama.sku_list import LlamaDownloadInfo
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from llama_stack.models.llama.sku_types import CheckpointQuantizationFormat
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class PromptGuardModel(BaseModel):
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"""Make a 'fake' Model-like object for Prompt Guard. Eventually this will be removed."""
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model_id: str
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huggingface_repo: str
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description: str = "Prompt Guard. NOTE: this model will not be provided via `llama` CLI soon."
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is_featured: bool = False
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max_seq_length: int = 512
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is_instruct_model: bool = False
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quantization_format: CheckpointQuantizationFormat = CheckpointQuantizationFormat.bf16
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arch_args: dict[str, Any] = Field(default_factory=dict)
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def descriptor(self) -> str:
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return self.model_id
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model_config = ConfigDict(protected_namespaces=())
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def prompt_guard_model_skus():
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return [
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PromptGuardModel(model_id="Prompt-Guard-86M", huggingface_repo="meta-llama/Prompt-Guard-86M"),
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PromptGuardModel(
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model_id="Llama-Prompt-Guard-2-86M",
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huggingface_repo="meta-llama/Llama-Prompt-Guard-2-86M",
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),
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PromptGuardModel(
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model_id="Llama-Prompt-Guard-2-22M",
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huggingface_repo="meta-llama/Llama-Prompt-Guard-2-22M",
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),
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]
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def prompt_guard_model_sku_map() -> dict[str, Any]:
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return {model.model_id: model for model in prompt_guard_model_skus()}
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def prompt_guard_download_info_map() -> dict[str, LlamaDownloadInfo]:
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return {
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model.model_id: LlamaDownloadInfo(
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folder="Prompt-Guard" if model.model_id == "Prompt-Guard-86M" else model.model_id,
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files=[
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"model.safetensors",
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"special_tokens_map.json",
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"tokenizer.json",
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"tokenizer_config.json",
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],
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pth_size=1,
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)
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for model in prompt_guard_model_skus()
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}
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