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Capabilities

Every question here is answerable before warmup(), because each reads configuration rather than running a forward. Ask instead of catching.

What this instance serves​

from interp_engine import load_model

model = load_model("Qwen/Qwen3-8B", backend="vllm-static", static_points="auto")

model.hooks_available          # can it capture or steer at all?
model.graph_replay             # CUDA graphs instead of Python forward?
model.static_points            # the declared tap set (empty unless static)
model.static_writes            # the declared steer sites
model.n_layers, model.d_model

Points on this model​

model.points() includes what the trunk adds, which importing the global table cannot.

from interp_engine import load_model

model = load_model("Qwen/Qwen3-8B")
for spec in model.points():
    print(spec.name, spec.vllm)

Gradients​

from interp_engine import load_model

model = load_model("google/gemma-2-2b-it", backend="eager", requires_grad=True)
print(model.grad_support.through_forward)
print(model.grad_support.describe())

Residual stream shape​

DeepSeek-V4 carries four parallel streams, so resid_post there names a stack no sublayer reads, and the logit lens does not apply.

from interp_engine import load_model

model = load_model("Qwen/Qwen3-8B")
basis = model.residual_basis
print(basis.n_streams, basis.additive, basis.lens_valid)
print(basis.describe())

Refusals​

Nothing degrades silently. Unsupported work raises CapabilityUnsupported, naming the capability, why this backend cannot serve it, and what to call instead.

from interp_engine import CAPABILITIES, CapabilityUnsupported, load_model, run_with_cache

for name, capability in CAPABILITIES.items():
    print(name, capability.why, capability.instead)

model = load_model("Qwen/Qwen3-8B", backend="vllm")
try:
    run_with_cache(model, model.to_tokens("Hi"), ["mlp_act.5"])
except CapabilityUnsupported as exc:
    print(exc)

Two things that are not portable​

  • GenStep.logits is filled on eager and None on vLLM, whose sampler never ships the tensor out of the worker. Ask for n_logprobs=k, which both honor.
  • The free decode_residuals is eager-only and returns raw logits. model.decode_residuals is the portable one — see Logit lens.