pllm.profiles Python API

Profiles provide reviewed slot-compatible pipeline presets while retaining the exact component identities used to compile an experiment. Presence documents API availability, not complete model, security, or deployment coverage; check implementation status before relying on a path.

Research context

Relevant design inputs: Slalom. PLLM's implementation and evidence claims remain independent.

Python SDK example

The example performs a bounded offline task and checks an observable result. It does not download a checkpoint or contact a provider.

import pllm.profiles as profiles

from pllm.sources import TinyModel

selected = profiles.MaskedLinearCpu(TinyModel("qwen2"))
assert selected.to_spec()["components"]["linear"]["component"] == "pllm/masked-linear"

API: pllm.profiles

Objects and signatures

DirectFHEProfile

Pipeline(model: 'Model', components: 'Mapping[str, ComponentRef]', profile: 'str | None' = None).

• Canonical object: pllm.profiles.DirectFHEProfile
• Kind: class
• Signature/type: __init__(self, model: ModelSource, *, linear: DirectFHE=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None
• Public exports: pllm.DirectFHEProfile, pllm.pipeline.DirectFHEProfile, pllm.profiles.DirectFHEProfile
• Public members: PROFILE: str; __init__(self, model: ModelSource, *, linear: DirectFHE=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None; property linear: DirectFHE; property inference: InferenceRole; property kernels: KernelBackend; get_params(self, deep: bool=True) -> dict[str, Any]; with_params(self, **changes: object) -> DirectFHEProfile

MaskedLinearCpu

Pipeline(model: 'Model', components: 'Mapping[str, ComponentRef]', profile: 'str | None' = None).

• Canonical object: pllm.profiles.MaskedLinearCpu
• Kind: class
• Signature/type: __init__(self, model: ModelSource, *, linear: ProtocolMethod=..., preparation: PreparationProvider=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None
• Public exports: pllm.MaskedLinearCpu, pllm.pipeline.MaskedLinearCpu, pllm.profiles.MaskedLinearCpu
• Public members: PROFILE: str; __init__(self, model: ModelSource, *, linear: ProtocolMethod=..., preparation: PreparationProvider=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None; property linear: ProtocolMethod; property preparation: PreparationProvider; property inference: InferenceRole; property kernels: KernelBackend; get_params(self, deep: bool=True) -> dict[str, Any]; with_params(self, **changes: object) -> MaskedLinearCpu

ProprietaryBlinded

Pipeline(model: 'Model', components: 'Mapping[str, ComponentRef]', profile: 'str | None' = None).

• Canonical object: pllm.profiles.ProprietaryBlinded
• Kind: class
• Signature/type: __init__(self, model: ModelSource, *, linear: BlindedLinear=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None
• Public exports: pllm.ProprietaryBlinded, pllm.pipeline.ProprietaryBlinded, pllm.profiles.ProprietaryBlinded
• Public members: PROFILE: str; __init__(self, model: ModelSource, *, linear: BlindedLinear=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None; property linear: BlindedLinear; property inference: InferenceRole; property kernels: KernelBackend; get_params(self, deep: bool=True) -> dict[str, Any]; with_params(self, **changes: object) -> ProprietaryBlinded

ProprietaryGuarded

Pipeline(model: 'Model', components: 'Mapping[str, ComponentRef]', profile: 'str | None' = None).

• Canonical object: pllm.profiles.ProprietaryGuarded
• Kind: class
• Signature/type: __init__(self, model: ModelSource, *, linear: GuardedLinear=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None
• Public exports: pllm.ProprietaryGuarded, pllm.pipeline.ProprietaryGuarded, pllm.profiles.ProprietaryGuarded
• Public members: PROFILE: str; __init__(self, model: ModelSource, *, linear: GuardedLinear=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None; property linear: GuardedLinear; property inference: InferenceRole; property kernels: KernelBackend; get_params(self, deep: bool=True) -> dict[str, Any]; with_params(self, **changes: object) -> ProprietaryGuarded

VerifiedMaskedLinearCpu

Pipeline(model: 'Model', components: 'Mapping[str, ComponentRef]', profile: 'str | None' = None).

• Canonical object: pllm.profiles.VerifiedMaskedLinearCpu
• Kind: class
• Signature/type: __init__(self, model: ModelSource, *, linear: ProtocolMethod=..., preparation: PreparationProvider=..., inference: InferenceRole=..., kernels: KernelBackend=..., verification: VerificationScheme=...) -> None
• Public exports: pllm.VerifiedMaskedLinearCpu, pllm.pipeline.VerifiedMaskedLinearCpu, pllm.profiles.VerifiedMaskedLinearCpu
• Public members: PROFILE: str; __init__(self, model: ModelSource, *, linear: ProtocolMethod=..., preparation: PreparationProvider=..., inference: InferenceRole=..., kernels: KernelBackend=..., verification: VerificationScheme=...) -> None; property linear: ProtocolMethod; property preparation: PreparationProvider; property inference: InferenceRole; property kernels: KernelBackend; property verification: FreivaldsVerify; with_params(self, **changes: object) -> VerifiedMaskedLinearCpu