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e4a1579e63
# What does this PR do? - Configured ruff linter to automatically fix import sorting issues. - Set --exit-non-zero-on-fix to ensure non-zero exit code when fixes are applied. - Enabled the 'I' selection to focus on import-related linting rules. - Ran the linter, and formatted all codebase imports accordingly. - Removed the black dep from the "dev" group since we use ruff Signed-off-by: Sébastien Han <seb@redhat.com> [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan [Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.*] [//]: # (## Documentation) [//]: # (- [ ] Added a Changelog entry if the change is significant) Signed-off-by: Sébastien Han <seb@redhat.com>
48 lines
1.5 KiB
Python
48 lines
1.5 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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from typing import Any, Dict, Optional
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from llama_models.datatypes import CheckpointQuantizationFormat
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from llama_models.llama3.api.datatypes import SamplingParams
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from llama_models.sku_list import LlamaDownloadInfo
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from pydantic import BaseModel, ConfigDict, Field
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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 = "Prompt-Guard-86M"
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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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huggingface_repo: str = "meta-llama/Prompt-Guard-86M"
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max_seq_length: int = 2048
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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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recommended_sampling_params: Optional[SamplingParams] = None
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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_sku():
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return PromptGuardModel()
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def prompt_guard_download_info():
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return LlamaDownloadInfo(
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folder="Prompt-Guard",
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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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