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Upload intrinsic README.

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@@ -1,10 +1,10 @@
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  # stembolts Intrinsic Adapter
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- This intrinsic adapter is designed to diagnose and predict potential issues in internal combustion engines based on a provided narrative description of symptoms. It identifies the likely defective part causing the issue and provides a likelihood score.
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  ## Training Data
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- The training dataset consists of JSONL formatted strings, where each string contains a narrative description of engine problems (symptoms) and associated metadata indicating the most likely faulty component and its diagnostic confidence level.
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  ### Examples
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@@ -68,14 +68,14 @@ class StemboltsAdapter(CustomGraniteCommonAdapter):
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  class StemboltsIntrinsic(Intrinsic, SimpleComponent):
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- def __init__(self, description: str):
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  Intrinsic.__init__(self, intrinsic_name=_INTRINSIC_ADAPTER_NAME)
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  SimpleComponent.__init__(self, mechanic_notes=mechanic_notes)
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  def format_for_llm(self):
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  return SimpleComponent.format_for_llm(self)
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- async def async_stembolts(description: str, ctx: Context, backend: Backend | AdapterMixin):
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  # Backend.add_adapter should be idempotent, but we'll go ahead and check just in case.
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  if adapter.qualified_name not in backend.list_adapters():
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  backend.add_adapter(StemboltsAdapter(backend.base_model_name))
@@ -84,11 +84,21 @@ async def async_stembolts(description: str, ctx: Context, backend: Backend | Ada
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  return mot
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- def stembolts(description: str, ctx: Context, backend: Backend | AdapterMixin):
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  # Backend.add_adapter should be idempotent, but we'll go ahead and check just in case.
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  adapter = StemboltsAdapter(backend.base_model_name)
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  if adapter.qualified_name not in backend.list_adapters():
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  backend.add_adapter(adapter)
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  action = StemboltsIntrinsic(notes)
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  return mfuncs.act(action, ctx, backend)
 
 
 
 
 
 
 
 
 
 
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  ```
 
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  # stembolts Intrinsic Adapter
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+ This intrinsic adapter is designed to diagnose and predict potential issues within internal combustion engines based on a variety of symptoms described in plain text. It uses a trained model to analyze these descriptions and identify the most likely defective part, along with an associated likelihood score.
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  ## Training Data
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+ The training dataset consists of JSONL formatted records where each record contains a free-form text description of engine issues and a corresponding labeled response indicating the suspected defective part and its diagnostic likelihood.
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  ### Examples
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  class StemboltsIntrinsic(Intrinsic, SimpleComponent):
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+ def __init__(self, (description: str) -> Dict[str, float]):
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  Intrinsic.__init__(self, intrinsic_name=_INTRINSIC_ADAPTER_NAME)
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  SimpleComponent.__init__(self, mechanic_notes=mechanic_notes)
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  def format_for_llm(self):
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  return SimpleComponent.format_for_llm(self)
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+ async def async_stembolts((description: str) -> Dict[str, float], ctx: Context, backend: Backend | AdapterMixin):
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  # Backend.add_adapter should be idempotent, but we'll go ahead and check just in case.
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  if adapter.qualified_name not in backend.list_adapters():
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  backend.add_adapter(StemboltsAdapter(backend.base_model_name))
 
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  return mot
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+ def stembolts((description: str) -> Dict[str, float], ctx: Context, backend: Backend | AdapterMixin):
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  # Backend.add_adapter should be idempotent, but we'll go ahead and check just in case.
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  adapter = StemboltsAdapter(backend.base_model_name)
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  if adapter.qualified_name not in backend.list_adapters():
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  backend.add_adapter(adapter)
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  action = StemboltsIntrinsic(notes)
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  return mfuncs.act(action, ctx, backend)
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+
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+ if __name__ == "__main__":
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+ from mellea.backends.huggingface import LocalHFBackend
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+ from mellea.backends.model_ids import IBM_GRANITE_4_MICRO_3B
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+ from mellea.stdlib.context import ChatContext
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+ backend = LocalHFBackend(IBM_GRANITE_4_MICRO_3B)
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+ # Example inputs: Airflow to the intake seems restricted; it bogs under throttle.
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+ # Example outputs: {"defective_part":"air filter","diag_likelihood":0.78}
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+ result, ctx = stembolts(..., ctx=ChatContext(), backend=backend)
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+ print(result.value)
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  ```