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Upload intrinsic README.

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  1. README.md +14 -7
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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 descriptive text inputs related to engine performance problems.
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  ## Training Data
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- The training dataset consists of plain string entries describing various symptoms and issues found in internal combustion engines. Each entry includes a predicted part that may be defective along with an associated likelihood score.
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  ### Examples
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@@ -68,14 +75,14 @@ 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))
@@ -84,12 +91,12 @@ 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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  if __name__ == "__main__":
 
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+ ---
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+ library_name: peft
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+ base_model: granite-4.0-micro
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+ tags:
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+ - mellea
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+ ---
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+
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  # stembolts Intrinsic Adapter
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+ This intrinsic adapter is designed to diagnose issues in internal combustion engines by analyzing user-provided descriptions of symptoms and returning likely defective parts along with a diagnostic likelihood score.
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  ## Training Data
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+ The training dataset consists of JSONL formatted records containing symptom descriptions and corresponding labels identifying the most likely 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)):
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  Intrinsic.__init__(self, intrinsic_name=_INTRINSIC_ADAPTER_NAME)
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+ SimpleComponent.__init__(self, description=description)
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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))
 
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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(description)
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  return mfuncs.act(action, ctx, backend)
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  if __name__ == "__main__":