[TRTLLM-15076][test] Remove Bielik-11B-v2.2-Instruct an…

tests/integration/defs/accuracy/references/gsm8k.yaml

@@ -305,8 +305,6 @@ nvidia/Llama-3_1-Nemotron-Ultra-253B-v1:
   - quant_algo: FP8
     kv_cache_quant_algo: FP8
     accuracy: 94.16
-kanana-1.5-2.1b-instruct-2505:
-  - accuracy: 75.81
 google/gemma-3-1b-it:
   - accuracy: 25.52 # score getting from lm-eval with HF implementation
   - quant_algo: FP8

tests/integration/defs/accuracy/references/mmlu.yaml

@@ -337,8 +337,6 @@ nvidia/Llama-3_1-Nemotron-Ultra-253B-v1:
   - quant_algo: FP8
     kv_cache_quant_algo: FP8
     accuracy: 83.36
-kanana-1.5-2.1b-instruct-2505:
-  - accuracy: 56.89
 mistralai/Ministral-8B-Instruct-2410:
   - accuracy: 66.35
   - quant_algo: FP8

tests/integration/defs/accuracy/test_llm_api_pytorch.py

@@ -5165,23 +5165,6 @@ def test_auto_dtype(self):
             task.evaluate(llm)
 
 
-class TestKanana_Instruct(LlmapiAccuracyTestHarness):
-    MODEL_NAME = "kanana-1.5-2.1b-instruct-2505"
-    MODEL_PATH = f"{llm_models_root()}/kanana-1.5-2.1b-instruct-2505"
-
-    @pytest.mark.skip_device_not_contain(["H20", "H100"])
-    def test_auto_dtype(self):
-        "RCCA: https://nvbugspro.nvidia.com/bug/5310520"
-        pytorch_config = dict(cuda_graph_config=CudaGraphConfig(
-            enable_padding=True, max_batch_size=384))
-        with LLM(self.MODEL_PATH, **pytorch_config,
-                 enable_attention_dp=True) as llm:
-            task = MMLU(self.MODEL_NAME)
-            task.evaluate(llm)
-            task = GSM8K(self.MODEL_NAME)
-            task.evaluate(llm)
-
-
 @skip_pre_hopper
 @pytest.mark.skip_less_device_memory(80000)
 class TestGPTOSS(LlmapiAccuracyTestHarness):

tests/integration/defs/perf/_model_paths.py

@@ -65,7 +65,6 @@
     "phi_4_multimodal_instruct": "multimodals/Phi-4-multimodal-instruct",
     "phi_4_multimodal_instruct_fp4": "multimodals/Phi-4-multimodal-instruct-FP4",
     "phi_4_multimodal_instruct_fp8": "multimodals/Phi-4-multimodal-instruct-FP8",
-    "bielik_11b_v2.2_instruct": "Bielik-11B-v2.2-Instruct",
     "gpt_oss_120b_fp4": "gpt_oss/gpt-oss-120b",
     "gpt_oss_20b_fp4": "gpt_oss/gpt-oss-20b",
     "gpt_oss_120b_eagle3": "gpt_oss/gpt-oss-120b",

tests/integration/test_lists/qa/llm_function_core.txt

@@ -532,7 +532,6 @@ accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_chunked_prefill[trtllm-aut
 accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_chunked_prefill[trtllm-fp8]
 accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4a16[dp4-auto]
 accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4a16[dp4-fp8]
-accuracy/test_llm_api_pytorch.py::TestKanana_Instruct::test_auto_dtype
 accuracy/test_llm_api_pytorch.py::TestKimiK25::test_nvfp4[dep8]
 accuracy/test_llm_api_pytorch.py::TestKimiK25::test_nvfp4[ep8]
 accuracy/test_llm_api_pytorch.py::TestKimiK25::test_nvfp4[tp8]

tests/integration/test_lists/test-db/l0_perf.yml

@@ -14,18 +14,3 @@ l0_perf:
         backend: pytorch
     tests:
       - perf/test_perf.py::test_perf[llama_v3.1_8b_instruct-bench-pytorch-float16-input_output_len:128,128-reqs:8192]
-
-  - condition:
-      ranges:
-        system_gpu_count:
-          gte: 1
-          lte: 1
-      wildcards:
-        gpu:
-          - '*l40s*'
-        linux_distribution_name: ubuntu*
-      terms:
-        stage: pre_merge
-        backend: pytorch
-    tests:
-      - perf/test_perf.py::test_perf[bielik_11b_v2.2_instruct-bench-pytorch-bfloat16-input_output_len:128,128]

tests/integration/test_lists/waives.txt

@@ -64,7 +64,7 @@ accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v2_kv_cache-dp4-cutl
 accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v2_kv_cache-ep4-cutlass-auto] SKIP (https://nvbugs/5596343)
 accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v2_kv_cache-tp4-cutlass-auto] SKIP (https://nvbugs/5596343)
 accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_chunked_prefill[cutlass-auto] SKIP (https://nvbugs/5596343)
-accuracy/test_llm_api_pytorch.py::TestKanana_Instruct::test_auto_dtype SKIP (https://nvbugs/6209806)
+accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_bfloat16_4gpus[pp4-attn_backend=TRTLLM-torch_compile=False] SKIP (https://nvbugs/6490043)
 accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_bfloat16_4gpus[tp2pp2-attn_backend=FLASHINFER-torch_compile=True] SKIP (https://nvbugs/6422337)
 accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_bfloat16_4gpus[tp4-attn_backend=TRTLLM-torch_compile=False] SKIP (https://nvbugs/5616182)
 accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_bfloat16_4gpus[tp4-attn_backend=TRTLLM-torch_compile=True] SKIP (https://nvbugs/6581073)

tests/unittest/llmapi/test_llm_pytorch.py

@@ -726,59 +726,6 @@ def test_llama_3_3_70b_fp8_with_squad_lora_tp2() -> None:
         llm.shutdown()
 
 
-@skip_gpu_memory_less_than_80gb
-@pytest.mark.part2
-@test_lora_with_and_without_cuda_graph
-def test_bielik_11b_v2_2_instruct_multi_lora(cuda_graph_config) -> None:
-    model_dir = f"{llm_models_root()}/Bielik-11B-v2.2-Instruct"
-
-    target_modules = ['attn_q', 'attn_k', 'attn_v']
-
-    # Set up temporary directory for LoRA adapters
-    with tempfile.TemporaryDirectory() as lora_dir:
-        print("Creating dummy LoRAs...")
-
-        model = AutoModelForCausalLM.from_pretrained(model_dir,
-                                                     dtype=torch.bfloat16,
-                                                     device_map="auto")
-        hf_modules = ["q_proj", "k_proj", "v_proj"]
-        peft_lora_config = PeftLoraConfig(r=8,
-                                          target_modules=hf_modules,
-                                          bias="none",
-                                          task_type="CAUSAL_LM")
-        lora_paths = []
-        for i in range(2):
-            lora_model = get_peft_model(model, peft_lora_config)
-            for param in lora_model.parameters():
-                param.data.zero_()
-            lora_path = f"{lora_dir}/lora_{i}"
-            lora_model.save_pretrained(lora_path)
-            lora_paths.append(lora_path)
-
-        trtllm_lora_config = LoraConfig(lora_target_modules=target_modules,
-                                        max_lora_rank=8,
-                                        max_loras=2,
-                                        max_cpu_loras=2)
-        llm = LLM(model_dir,
-                  lora_config=trtllm_lora_config,
-                  cuda_graph_config=cuda_graph_config)
-
-        prompts = [
-            "Kim był Mikołaj Kopernik i z czego zasłynął?",
-            "Gdzie znajduje się stolica Polski?",
-        ]
-        lora_req1 = LoRARequest("lora-1", 0, lora_paths[0])
-        lora_req2 = LoRARequest("lora-2", 1, lora_paths[1])
-        lora_requests = [lora_req1, lora_req2]
-        sampling_params = SamplingParams(max_tokens=200)
-
-        outputs = llm.generate(prompts,
-                               sampling_params,
-                               lora_request=lora_requests)
-
-        assert len(outputs) == 2
-
-
 @pytest.mark.part2
 @test_lora_with_and_without_cuda_graph
 def test_gemma3_1b_instruct_multi_lora(cuda_graph_config) -> None: