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import torch
from cognitive_mapping_probe.llm_iface import get_or_load_model
from cognitive_mapping_probe.resonance_seismograph import run_silent_cogitation_seismic
from cognitive_mapping_probe.concepts import get_concept_vector, _get_last_token_hidden_state
def test_get_or_load_model_loads_correctly(model_id):
"""Testet, ob das Laden eines echten Modells funktioniert."""
llm = get_or_load_model(model_id, seed=42)
assert llm is not None
assert llm.model_id == model_id
assert llm.stable_config.hidden_dim > 0
assert llm.stable_config.num_layers > 0
def test_run_silent_cogitation_seismic_output_shape_and_type(model_id):
"""Führt einen kurzen Lauf mit einem echten Modell durch und prüft die Datentypen."""
num_steps = 5
llm = get_or_load_model(model_id, seed=42)
state_deltas = run_silent_cogitation_seismic(
llm=llm, prompt_type="control_long_prose",
num_steps=num_steps, temperature=0.1
)
assert isinstance(state_deltas, list)
assert len(state_deltas) == num_steps
assert all(isinstance(d, float) for d in state_deltas)
def test_get_last_token_hidden_state_robustness(model_id):
"""Testet die Helper-Funktion mit einem echten Modell."""
llm = get_or_load_model(model_id, seed=42)
hs = _get_last_token_hidden_state(llm, "test prompt")
assert isinstance(hs, torch.Tensor)
assert hs.shape == (llm.stable_config.hidden_dim,)
def test_get_concept_vector_logic(model_id):
"""Testet die Vektor-Extraktion mit einem echten Modell."""
llm = get_or_load_model(model_id, seed=42)
# Verwende eine sehr kurze Baseline für einen schnellen Test
vector = get_concept_vector(llm, "love", baseline_words=["thing", "place"])
assert isinstance(vector, torch.Tensor)
assert vector.shape == (llm.stable_config.hidden_dim,)
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