google/gemma-2-2b-it · TypeError: 'NoneType' object is not callable

!vllm serve "google/gemma-2-2b-it" --dtype=half

Error:
INFO 01-14 09:41:39 api_server.py:219] vLLM API server version 0.5.3.post1
INFO 01-14 09:41:39 api_server.py:220] args: Namespace(model_tag='/kaggle/input/gemma/transformers/2b-it/3', host=None, port=8000, uvicorn_log_level='info', allow_credentials=False, allowed_origins=[''], allowed_methods=[''], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], model='/kaggle/input/gemma/transformers/2b-it/3', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, download_dir=None, load_format='auto', dtype='half', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=None, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=1, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, disable_sliding_window=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=False, max_context_len_to_capture=None, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_model=None, num_speculative_tokens=None, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, engine_use_ray=False, disable_log_requests=False, max_log_len=None, dispatch_function=<function serve at 0x7b974351add0>)
WARNING 01-14 09:41:39 config.py:1425] Casting torch.bfloat16 to torch.float16.
INFO 01-14 09:41:39 llm_engine.py:176] Initializing an LLM engine (v0.5.3.post1) with config: model='/kaggle/input/gemma/transformers/2b-it/3', speculative_config=None, tokenizer='/kaggle/input/gemma/transformers/2b-it/3', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None), seed=0, served_model_name=/kaggle/input/gemma/transformers/2b-it/3, use_v2_block_manager=False, enable_prefix_caching=False)
INFO 01-14 09:41:40 selector.py:80] Using Flashinfer backend.
INFO 01-14 09:41:41 model_runner.py:680] Starting to load model /kaggle/input/gemma/transformers/2b-it/3...
INFO 01-14 09:41:41 selector.py:80] Using Flashinfer backend.
WARNING 01-14 09:41:41 gemma.py:56] Gemma's activation function was incorrectly set to exact GeLU in the config JSON file when it was initially released. Changing the activation function to approximate GeLU (gelu_pytorch_tanh). If you want to use the legacy gelu, edit the config JSON to set hidden_activation=gelu instead of hidden_act. See https://github.com/huggingface/transformers/pull/29402 for more details.
Loading safetensors checkpoint shards: 0% Completed | 0/2 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 50% Completed | 1/2 [00:23<00:23, 23.04s/it]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:23<00:00, 9.81s/it]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:23<00:00, 11.79s/it]

INFO 01-14 09:42:04 model_runner.py:692] Loading model weights took 4.7384 GB
[rank0]: Traceback (most recent call last):
[rank0]: File "/usr/local/bin/vllm", line 8, in
[rank0]: sys.exit(main())
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/scripts.py", line 148, in main
[rank0]: args.dispatch_function(args)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/scripts.py", line 28, in serve
[rank0]: run_server(args)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/api_server.py", line 231, in run_server
[rank0]: if llm_engine is not None else AsyncLLMEngine.from_engine_args(
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 466, in from_engine_args
[rank0]: engine = cls(
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 380, in init
[rank0]: self.engine = self._init_engine(*args, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 547, in _init_engine
[rank0]: return engine_class(*args, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 265, in init
[rank0]: self._initialize_kv_caches()
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 364, in _initialize_kv_caches
[rank0]: self.model_executor.determine_num_available_blocks())
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py", line 94, in determine_num_available_blocks
[rank0]: return self.driver_worker.determine_num_available_blocks()
[rank0]: File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]: return func(*args, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 179, in determine_num_available_blocks
[rank0]: self.model_runner.profile_run()
[rank0]: File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]: return func(*args, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 896, in profile_run
[rank0]: self.execute_model(model_input, kv_caches, intermediate_tensors)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]: return func(*args, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 1272, in execute_model
[rank0]: BatchDecodeWithPagedKVCacheWrapper(
[rank0]: TypeError: 'NoneType' object is not callable