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https://github.com/NicolasBohn/NexQuant.git
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XianBW
63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
from pathlib import Path
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from rdagent.components.coder.model_coder.model import ModelExperiment
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from rdagent.core.prompts import Prompts
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from rdagent.core.scenario import Scenario
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prompt_dict = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
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class GeneralModelScenario(Scenario):
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@property
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def background(self) -> str:
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return prompt_dict["general_model_background"]
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@property
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def source_data(self) -> str:
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raise NotImplementedError("source_data of GeneralModelScenario is not implemented")
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@property
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def output_format(self) -> str:
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return prompt_dict["general_model_output_format"]
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@property
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def interface(self) -> str:
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return prompt_dict["general_model_interface"]
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@property
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def simulator(self) -> str:
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return prompt_dict["general_model_simulator"]
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@property
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def rich_style_description(self) -> str:
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return """
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### [Model Research & Development Co-Pilot](#_scenario)
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#### [Overview](#_summary)
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This demo automates the extraction and development of PyTorch models from academic papers. It supports various model types through two main components: Reader and Coder.
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#### [Workflow Components](#_rdloops)
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1. **[Reader](#_research)**
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- Extracts model information from papers, including architectures and parameters.
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- Converts content into a structured format using Large Language Models.
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2. **[Evolving Coder](#_development)**
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- Translates structured information into executable PyTorch code.
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- Ensures correct tensor shapes with an evolving coding mechanism.
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- Refines the code to match source specifications.
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"""
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def get_scenario_all_desc(self) -> str:
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return f"""Background of the scenario:
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{self.background}
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The interface you should follow to write the runnable code:
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{self.interface}
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The output of your code should be in the format:
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{self.output_format}
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The simulator user can use to test your model:
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{self.simulator}
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"""
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