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2 changes: 1 addition & 1 deletion tests/integration/defs/accuracy/accuracy_core.py
Original file line number Diff line number Diff line change
Expand Up @@ -452,7 +452,7 @@ class LongBenchV2(AccuracyTask):
EVALUATOR_KWARGS = dict(
dataset_path=DATASET_DIR,
length="medium",
max_len=120000,
max_input_length=120000,
apply_chat_template=True,
random_seed=0,
)
Expand Down
3 changes: 3 additions & 0 deletions tests/integration/defs/accuracy/test_llm_api_pytorch.py
Original file line number Diff line number Diff line change
Expand Up @@ -1118,6 +1118,7 @@ def test_fp8_prequantized(self):
task = MMLU(self.MODEL_NAME)
task.evaluate(llm)

@skip_pre_hopper
def test_fp8_vswa_reuse(self):
# NOTE: Test with VSWA kv cache config.
kv_cache_config = KvCacheConfig(
Expand All @@ -1132,6 +1133,7 @@ def test_fp8_vswa_reuse(self):
task = MMLU(self.MODEL_NAME)
task.evaluate(llm)

@skip_pre_hopper
@pytest.mark.parametrize("backend", ["xgrammar"])
def test_fp8_guided_decoding_vswa_reuse(self, backend: str, mocker):
mocker.patch.dict(os.environ, {"TRTLLM_XGUIDANCE_LENIENT": "1"})
Expand Down Expand Up @@ -5276,6 +5278,7 @@ class TestNemotronV3Nano(LlmapiAccuracyTestHarness):
EXTRA_EVALUATOR_KWARGS = dict(chat_template_kwargs=dict(
enable_thinking=False))

@pytest.mark.skip_less_device_memory(80000)
def test_auto_dtype(self):
with LLM(
f"{llm_models_root()}/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
Expand Down