logs/vllm_server.log

INFO 07-22 22:49:16 [__init__.py:235] Automatically detected platform cuda.
INFO 07-22 22:49:18 [api_server.py:1755] vLLM API server version 0.10.1.dev1+gbcc0a3cbe
INFO 07-22 22:49:18 [cli_args.py:261] non-default args: {'model': '/tmp/judge_model', 'max_model_len': 4096, 'served_model_name': ['model/Qwen2.5-72B-Instruct'], 'tensor_parallel_size': 4, 'gpu_memory_utilization': 0.95, 'max_num_seqs': 64}
INFO 07-22 22:49:26 [config.py:1604] Using max model len 4096
INFO 07-22 22:49:26 [config.py:2434] Chunked prefill is enabled with max_num_batched_tokens=2048.
INFO 07-22 22:49:31 [__init__.py:235] Automatically detected platform cuda.
INFO 07-22 22:49:33 [core.py:572] Waiting for init message from front-end.
INFO 07-22 22:49:33 [core.py:71] Initializing a V1 LLM engine (v0.10.1.dev1+gbcc0a3cbe) with config: model='/tmp/judge_model', speculative_config=None, tokenizer='/tmp/judge_model', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config={}, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=4, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto,  device_config=cuda, decoding_config=DecodingConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_backend=''), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=model/Qwen2.5-72B-Instruct, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=True, chunked_prefill_enabled=True, use_async_output_proc=True, pooler_config=None, compilation_config={"level":3,"debug_dump_path":"","cache_dir":"","backend":"","custom_ops":[],"splitting_ops":["vllm.unified_attention","vllm.unified_attention_with_output","vllm.mamba_mixer2"],"use_inductor":true,"compile_sizes":[],"inductor_compile_config":{"enable_auto_functionalized_v2":false},"inductor_passes":{},"use_cudagraph":true,"cudagraph_num_of_warmups":1,"cudagraph_capture_sizes":[128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"cudagraph_copy_inputs":false,"full_cuda_graph":false,"max_capture_size":128,"local_cache_dir":null}
WARNING 07-22 22:49:33 [multiproc_worker_utils.py:307] Reducing Torch parallelism from 48 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed.
INFO 07-22 22:49:33 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3], buffer_handle=(4, 16777216, 10, 'psm_52ba188f'), local_subscribe_addr='ipc:///tmp/db2bec79-ff52-47fd-bf2e-d5eef50b7d9f', remote_subscribe_addr=None, remote_addr_ipv6=False)
INFO 07-22 22:49:37 [__init__.py:235] Automatically detected platform cuda.
INFO 07-22 22:49:37 [__init__.py:235] Automatically detected platform cuda.
INFO 07-22 22:49:37 [__init__.py:235] Automatically detected platform cuda.
INFO 07-22 22:49:37 [__init__.py:235] Automatically detected platform cuda.
(VllmWorker rank=2 pid=851) INFO 07-22 22:49:42 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_a70570d3'), local_subscribe_addr='ipc:///tmp/628e138b-89db-4155-aca7-5206b4e3cd15', remote_subscribe_addr=None, remote_addr_ipv6=False)
(VllmWorker rank=1 pid=850) INFO 07-22 22:49:42 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_bc30d664'), local_subscribe_addr='ipc:///tmp/f88151f9-aed4-42a2-9404-b3687da07664', remote_subscribe_addr=None, remote_addr_ipv6=False)
(VllmWorker rank=0 pid=849) (VllmWorker rank=3 pid=852) INFO 07-22 22:49:42 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_54c42680'), local_subscribe_addr='ipc:///tmp/95d2d387-8297-4844-82cb-4f3d5d187ea3', remote_subscribe_addr=None, remote_addr_ipv6=False)
INFO 07-22 22:49:42 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_19701a5b'), local_subscribe_addr='ipc:///tmp/9cd5b4a8-837b-49c0-a5a9-7b6c22cdc179', remote_subscribe_addr=None, remote_addr_ipv6=False)
(VllmWorker rank=2 pid=851) INFO 07-22 22:49:44 [__init__.py:1375] Found nccl from library libnccl.so.2
(VllmWorker rank=0 pid=849) INFO 07-22 22:49:44 [__init__.py:1375] Found nccl from library libnccl.so.2
(VllmWorker rank=1 pid=850) INFO 07-22 22:49:44 [__init__.py:1375] Found nccl from library libnccl.so.2
(VllmWorker rank=3 pid=852) INFO 07-22 22:49:44 [__init__.py:1375] Found nccl from library libnccl.so.2
(VllmWorker rank=2 pid=851) INFO 07-22 22:49:44 [pynccl.py:70] vLLM is using nccl==2.26.2
(VllmWorker rank=0 pid=849) INFO 07-22 22:49:44 [pynccl.py:70] vLLM is using nccl==2.26.2
(VllmWorker rank=1 pid=850) INFO 07-22 22:49:44 [pynccl.py:70] vLLM is using nccl==2.26.2
(VllmWorker rank=3 pid=852) INFO 07-22 22:49:44 [pynccl.py:70] vLLM is using nccl==2.26.2
(VllmWorker rank=0 pid=849) INFO 07-22 22:49:44 [custom_all_reduce_utils.py:208] generating GPU P2P access cache in /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3.json
(VllmWorker rank=0 pid=849) INFO 07-22 22:50:00 [custom_all_reduce_utils.py:246] reading GPU P2P access cache from /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3.json
(VllmWorker rank=2 pid=851) INFO 07-22 22:50:00 [custom_all_reduce_utils.py:246] reading GPU P2P access cache from /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3.json
(VllmWorker rank=3 pid=852) (VllmWorker rank=1 pid=850) INFO 07-22 22:50:00 [custom_all_reduce_utils.py:246] reading GPU P2P access cache from /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3.json
INFO 07-22 22:50:00 [custom_all_reduce_utils.py:246] reading GPU P2P access cache from /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3.json
(VllmWorker rank=0 pid=849) INFO 07-22 22:50:00 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3], buffer_handle=(3, 4194304, 6, 'psm_78762edb'), local_subscribe_addr='ipc:///tmp/c2311839-dfca-46db-bf2e-2d965db3054b', remote_subscribe_addr=None, remote_addr_ipv6=False)
(VllmWorker rank=1 pid=850) INFO 07-22 22:50:00 [parallel_state.py:1102] rank 1 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 1, EP rank 1
(VllmWorker rank=0 pid=849) (VllmWorker rank=2 pid=851) INFO 07-22 22:50:00 [parallel_state.py:1102] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 0, EP rank 0
(VllmWorker rank=3 pid=852) INFO 07-22 22:50:00 [parallel_state.py:1102] rank 2 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 2, EP rank 2
INFO 07-22 22:50:00 [parallel_state.py:1102] rank 3 in world size 4 is assigned as DP rank 0, PP rank 0, TP rank 3, EP rank 3
(VllmWorker rank=1 pid=850) INFO 07-22 22:50:00 [topk_topp_sampler.py:49] Using FlashInfer for top-p & top-k sampling.
(VllmWorker rank=3 pid=852) INFO 07-22 22:50:00 [topk_topp_sampler.py:49] Using FlashInfer for top-p & top-k sampling.
(VllmWorker rank=0 pid=849) INFO 07-22 22:50:00 [topk_topp_sampler.py:49] Using FlashInfer for top-p & top-k sampling.
(VllmWorker rank=2 pid=851) INFO 07-22 22:50:00 [topk_topp_sampler.py:49] Using FlashInfer for top-p & top-k sampling.
(VllmWorker rank=1 pid=850) INFO 07-22 22:50:00 [gpu_model_runner.py:1843] Starting to load model /tmp/judge_model...
(VllmWorker rank=3 pid=852) INFO 07-22 22:50:00 [gpu_model_runner.py:1843] Starting to load model /tmp/judge_model...
(VllmWorker rank=2 pid=851) INFO 07-22 22:50:00 [gpu_model_runner.py:1843] Starting to load model /tmp/judge_model...
(VllmWorker rank=0 pid=849) INFO 07-22 22:50:00 [gpu_model_runner.py:1843] Starting to load model /tmp/judge_model...
(VllmWorker rank=3 pid=852) INFO 07-22 22:50:00 [gpu_model_runner.py:1875] Loading model from scratch...
(VllmWorker rank=1 pid=850) INFO 07-22 22:50:00 [gpu_model_runner.py:1875] Loading model from scratch...
(VllmWorker rank=0 pid=849) INFO 07-22 22:50:00 [gpu_model_runner.py:1875] Loading model from scratch...
(VllmWorker rank=2 pid=851) INFO 07-22 22:50:00 [gpu_model_runner.py:1875] Loading model from scratch...
(VllmWorker rank=3 pid=852) INFO 07-22 22:50:00 [cuda.py:290] Using Flash Attention backend on V1 engine.
(VllmWorker rank=1 pid=850) INFO 07-22 22:50:00 [cuda.py:290] Using Flash Attention backend on V1 engine.
(VllmWorker rank=0 pid=849) INFO 07-22 22:50:00 [cuda.py:290] Using Flash Attention backend on V1 engine.
(VllmWorker rank=2 pid=851) INFO 07-22 22:50:01 [cuda.py:290] Using Flash Attention backend on V1 engine.
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(VllmWorker rank=0 pid=849) 
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(VllmWorker rank=0 pid=849) (VllmWorker rank=2 pid=851) INFO 07-22 22:50:17 [default_loader.py:262] Loading weights took 16.20 seconds

(VllmWorker rank=0 pid=849) 
Loading safetensors checkpoint shards:  89% Completed | 33/37 [00:16<00:01,  2.05it/s]
(VllmWorker rank=0 pid=849) (VllmWorker rank=2 pid=851) INFO 07-22 22:50:18 [gpu_model_runner.py:1892] Model loading took 33.9836 GiB and 16.577510 seconds

(VllmWorker rank=0 pid=849) 
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(VllmWorker rank=0 pid=849) INFO 07-22 22:50:19 [default_loader.py:262] Loading weights took 18.13 seconds
(VllmWorker rank=3 pid=852) INFO 07-22 22:50:19 [default_loader.py:262] Loading weights took 18.25 seconds
(VllmWorker rank=1 pid=850) INFO 07-22 22:50:19 [default_loader.py:262] Loading weights took 18.56 seconds
(VllmWorker rank=0 pid=849) INFO 07-22 22:50:19 [gpu_model_runner.py:1892] Model loading took 33.9836 GiB and 18.521817 seconds
(VllmWorker rank=3 pid=852) INFO 07-22 22:50:20 [gpu_model_runner.py:1892] Model loading took 33.9836 GiB and 18.661305 seconds
(VllmWorker rank=1 pid=850) INFO 07-22 22:50:20 [gpu_model_runner.py:1892] Model loading took 33.9836 GiB and 18.954326 seconds
(VllmWorker rank=2 pid=851) INFO 07-22 22:50:37 [backends.py:530] Using cache directory: /root/.cache/vllm/torch_compile_cache/cc1a407bfb/rank_2_0/backbone for vLLM's torch.compile
(VllmWorker rank=2 pid=851) INFO 07-22 22:50:37 [backends.py:541] Dynamo bytecode transform time: 17.35 s
(VllmWorker rank=1 pid=850) INFO 07-22 22:50:38 [backends.py:530] Using cache directory: /root/.cache/vllm/torch_compile_cache/cc1a407bfb/rank_1_0/backbone for vLLM's torch.compile
(VllmWorker rank=1 pid=850) INFO 07-22 22:50:38 [backends.py:541] Dynamo bytecode transform time: 17.57 s
(VllmWorker rank=3 pid=852) INFO 07-22 22:50:38 [backends.py:530] Using cache directory: /root/.cache/vllm/torch_compile_cache/cc1a407bfb/rank_3_0/backbone for vLLM's torch.compile
(VllmWorker rank=3 pid=852) INFO 07-22 22:50:38 [backends.py:541] Dynamo bytecode transform time: 17.62 s
(VllmWorker rank=0 pid=849) INFO 07-22 22:50:38 [backends.py:530] Using cache directory: /root/.cache/vllm/torch_compile_cache/cc1a407bfb/rank_0_0/backbone for vLLM's torch.compile
(VllmWorker rank=0 pid=849) INFO 07-22 22:50:38 [backends.py:541] Dynamo bytecode transform time: 17.69 s
(VllmWorker rank=2 pid=851) INFO 07-22 22:50:42 [backends.py:194] Cache the graph for dynamic shape for later use
(VllmWorker rank=1 pid=850) INFO 07-22 22:50:42 [backends.py:194] Cache the graph for dynamic shape for later use
(VllmWorker rank=0 pid=849) INFO 07-22 22:50:42 [backends.py:194] Cache the graph for dynamic shape for later use
(VllmWorker rank=3 pid=852) INFO 07-22 22:50:42 [backends.py:194] Cache the graph for dynamic shape for later use
(VllmWorker rank=1 pid=850) INFO 07-22 22:51:41 [backends.py:215] Compiling a graph for dynamic shape takes 61.88 s
(VllmWorker rank=0 pid=849) INFO 07-22 22:51:41 [backends.py:215] Compiling a graph for dynamic shape takes 61.82 s
(VllmWorker rank=2 pid=851) INFO 07-22 22:51:41 [backends.py:215] Compiling a graph for dynamic shape takes 62.74 s
(VllmWorker rank=3 pid=852) INFO 07-22 22:51:42 [backends.py:215] Compiling a graph for dynamic shape takes 63.04 s
(VllmWorker rank=0 pid=849) INFO 07-22 22:51:52 [monitor.py:34] torch.compile takes 79.52 s in total
(VllmWorker rank=3 pid=852) INFO 07-22 22:51:52 [monitor.py:34] torch.compile takes 80.66 s in total
(VllmWorker rank=1 pid=850) INFO 07-22 22:51:52 [monitor.py:34] torch.compile takes 79.45 s in total
(VllmWorker rank=2 pid=851) INFO 07-22 22:51:52 [monitor.py:34] torch.compile takes 80.10 s in total
(VllmWorker rank=0 pid=849) /usr/local/lib/python3.12/dist-packages/torch/utils/cpp_extension.py:2356: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. 
(VllmWorker rank=0 pid=849) If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].
(VllmWorker rank=0 pid=849)   warnings.warn(
(VllmWorker rank=1 pid=850) /usr/local/lib/python3.12/dist-packages/torch/utils/cpp_extension.py:2356: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. 
(VllmWorker rank=1 pid=850) If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].
(VllmWorker rank=1 pid=850)   warnings.warn(
(VllmWorker rank=2 pid=851) /usr/local/lib/python3.12/dist-packages/torch/utils/cpp_extension.py:2356: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. 
(VllmWorker rank=3 pid=852) (VllmWorker rank=2 pid=851) /usr/local/lib/python3.12/dist-packages/torch/utils/cpp_extension.py:2356: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. 
If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].
(VllmWorker rank=2 pid=851) (VllmWorker rank=3 pid=852)   warnings.warn(
If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].
(VllmWorker rank=3 pid=852)   warnings.warn(
(VllmWorker rank=1 pid=850) INFO 07-22 22:51:53 [gpu_worker.py:255] Available KV cache memory: 39.29 GiB
(VllmWorker rank=0 pid=849) INFO 07-22 22:51:53 [gpu_worker.py:255] Available KV cache memory: 39.58 GiB
(VllmWorker rank=2 pid=851) INFO 07-22 22:51:53 [gpu_worker.py:255] Available KV cache memory: 39.29 GiB
(VllmWorker rank=3 pid=852) INFO 07-22 22:51:53 [gpu_worker.py:255] Available KV cache memory: 39.58 GiB
INFO 07-22 22:51:54 [kv_cache_utils.py:833] GPU KV cache size: 518,720 tokens
INFO 07-22 22:51:54 [kv_cache_utils.py:837] Maximum concurrency for 4,096 tokens per request: 126.64x
INFO 07-22 22:51:54 [kv_cache_utils.py:833] GPU KV cache size: 515,040 tokens
INFO 07-22 22:51:54 [kv_cache_utils.py:837] Maximum concurrency for 4,096 tokens per request: 125.74x
INFO 07-22 22:51:54 [kv_cache_utils.py:833] GPU KV cache size: 515,040 tokens
INFO 07-22 22:51:54 [kv_cache_utils.py:837] Maximum concurrency for 4,096 tokens per request: 125.74x
INFO 07-22 22:51:54 [kv_cache_utils.py:833] GPU KV cache size: 518,720 tokens
INFO 07-22 22:51:54 [kv_cache_utils.py:837] Maximum concurrency for 4,096 tokens per request: 126.64x
(VllmWorker rank=0 pid=849) 
(VllmWorker rank=0 pid=849) 
Capturing CUDA graph shapes:   0%|          | 0/19 [00:00<?, ?it/s]
(VllmWorker rank=0 pid=849) 
Capturing CUDA graph shapes:  58%|█████▊    | 11/19 [00:01<00:00, 10.88it/s]
Capturing CUDA graph shapes: 100%|██████████| 19/19 [00:01<00:00, 10.85it/s]
(VllmWorker rank=1 pid=850) INFO 07-22 22:51:56 [custom_all_reduce.py:196] Registering 3059 cuda graph addresses
(VllmWorker rank=0 pid=849) INFO 07-22 22:51:56 [custom_all_reduce.py:196] Registering 3059 cuda graph addresses
(VllmWorker rank=2 pid=851) INFO 07-22 22:51:56 [custom_all_reduce.py:196] Registering 3059 cuda graph addresses
(VllmWorker rank=3 pid=852) INFO 07-22 22:51:56 [custom_all_reduce.py:196] Registering 3059 cuda graph addresses
(VllmWorker rank=1 pid=850) INFO 07-22 22:51:56 [gpu_model_runner.py:2485] Graph capturing finished in 2 secs, took 0.32 GiB
(VllmWorker rank=0 pid=849) INFO 07-22 22:51:56 [gpu_model_runner.py:2485] Graph capturing finished in 2 secs, took 0.32 GiB
(VllmWorker rank=3 pid=852) INFO 07-22 22:51:56 [gpu_model_runner.py:2485] Graph capturing finished in 2 secs, took 0.32 GiB
(VllmWorker rank=2 pid=851) INFO 07-22 22:51:56 [gpu_model_runner.py:2485] Graph capturing finished in 2 secs, took 0.32 GiB
INFO 07-22 22:51:56 [core.py:193] init engine (profile, create kv cache, warmup model) took 96.63 seconds
INFO 07-22 22:51:57 [loggers.py:141] Engine 000: vllm cache_config_info with initialization after num_gpu_blocks is: 32190
WARNING 07-22 22:51:57 [config.py:1528] Default sampling parameters have been overridden by the model's Hugging Face generation config recommended from the model creator. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`.
INFO 07-22 22:51:57 [serving_responses.py:89] Using default chat sampling params from model: {'repetition_penalty': 1.05, 'temperature': 0.7, 'top_k': 20, 'top_p': 0.8}
INFO 07-22 22:51:57 [serving_chat.py:122] Using default chat sampling params from model: {'repetition_penalty': 1.05, 'temperature': 0.7, 'top_k': 20, 'top_p': 0.8}
INFO 07-22 22:51:57 [serving_completion.py:77] Using default completion sampling params from model: {'repetition_penalty': 1.05, 'temperature': 0.7, 'top_k': 20, 'top_p': 0.8}
INFO 07-22 22:51:57 [api_server.py:1818] Starting vLLM API server 0 on http://0.0.0.0:8000
INFO 07-22 22:51:57 [launcher.py:29] Available routes are:
INFO 07-22 22:51:57 [launcher.py:37] Route: /openapi.json, Methods: HEAD, GET
INFO 07-22 22:51:57 [launcher.py:37] Route: /docs, Methods: HEAD, GET
INFO 07-22 22:51:57 [launcher.py:37] Route: /docs/oauth2-redirect, Methods: HEAD, GET
INFO 07-22 22:51:57 [launcher.py:37] Route: /redoc, Methods: HEAD, GET
INFO 07-22 22:51:57 [launcher.py:37] Route: /health, Methods: GET
INFO 07-22 22:51:57 [launcher.py:37] Route: /load, Methods: GET
INFO 07-22 22:51:57 [launcher.py:37] Route: /ping, Methods: POST
INFO 07-22 22:51:57 [launcher.py:37] Route: /ping, Methods: GET
INFO 07-22 22:51:57 [launcher.py:37] Route: /tokenize, Methods: POST
INFO 07-22 22:51:57 [launcher.py:37] Route: /detokenize, Methods: POST
INFO 07-22 22:51:57 [launcher.py:37] Route: /v1/models, Methods: GET
INFO 07-22 22:51:57 [launcher.py:37] Route: /version, Methods: GET
INFO 07-22 22:51:57 [launcher.py:37] Route: /v1/responses, Methods: POST
INFO 07-22 22:51:57 [launcher.py:37] Route: /v1/responses/{response_id}, Methods: GET
INFO 07-22 22:51:57 [launcher.py:37] Route: /v1/responses/{response_id}/cancel, Methods: POST
INFO 07-22 22:51:57 [launcher.py:37] Route: /v1/chat/completions, Methods: POST
INFO 07-22 22:51:57 [launcher.py:37] Route: /v1/completions, Methods: POST
INFO 07-22 22:51:57 [launcher.py:37] Route: /v1/embeddings, Methods: POST
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INFO:     Started server process [444]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     127.0.0.1:39156 - "GET /health HTTP/1.1" 200 OK
INFO 07-22 22:52:09 [chat_utils.py:473] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-b337854fb3de435083070bcb2da27783: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220715",\n    "weekday": "Friday",\n    "report": "Hot and mostly sunny weather is expected today. A few thunderstorms will be possible this afternoon and evening, mainly over the eastern plains."\n  },\n  {\n    "date": "20220716",\n    "weekday": "Saturday",\n    "report": "Hot and mostly sunny weather is expected Saturday. A few thunderstorms will be possible this afternoon and evening, mainly over the eastern plains."\n  },\n  {\n    "date": "20220717",\n    "weekday": "Sunday",\n    "report": "A few thunderstorms are expected Sunday."\n  },\n  {\n    "date": "20220718",\n    "weekday": "Monday",\n    "report": "A few thunderstorms are expected Monday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2474, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-b337854fb3de435083070bcb2da27783.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-146af99f2ca84d05ae5c132a4bc76f41: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220720",\n    "weekday": "Wednesday",\n    "report": "High pressure will build over the region today."\n  },\n  {\n    "date": "20220721",\n    "weekday": "Thursday",\n    "report": "High pressure will build over the region through Thursday."\n  },\n  {\n    "date": "20220722",\n    "weekday": "Friday",\n    "report": "High pressure will build over the region Friday."\n  },\n  {\n    "date": "20220723",\n    "weekday": "Saturday",\n    "report": "A low pressure system will bring a chance of thunderstorms to the eastern plains Saturday. Drier conditions will return Saturday night."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2491, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-146af99f2ca84d05ae5c132a4bc76f41.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-0a2df9f92393484f9354682d7f981368: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220108",\n    "weekday": "Saturday",\n    "report": "Breezy conditions will continue today, with temperatures remaining near normal."\n  },\n  {\n    "date": "20220109",\n    "weekday": "Sunday",\n    "report": "A cold front will move through Sunday morning, bringing a significant drop in temperatures."\n  },\n  {\n    "date": "20220110",\n    "weekday": "Monday",\n    "report": "High pressure will build in from the west Monday, bringing a warming trend."\n  },\n  {\n    "date": "20220111",\n    "weekday": "Tuesday",\n    "report": "No forecast available"\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2495, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-0a2df9f92393484f9354682d7f981368.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-2441171d83884e6b9b4c5ad2e60e5a24: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20221008",\n    "weekday": "Saturday",\n    "report": "A few showers and thunderstorms will be possible today."\n  },\n  {\n    "date": "20221009",\n    "weekday": "Sunday",\n    "report": "A few showers and thunderstorms will be possible Sunday."\n  },\n  {\n    "date": "20221010",\n    "weekday": "Monday",\n    "report": "A few showers and thunderstorms will be possible Monday."\n  },\n  {\n    "date": "20221011",\n    "weekday": "Tuesday",\n    "report": "Drier conditions are expected Tuesday, with showers and thunderstorms becoming more isolated."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2495, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-2441171d83884e6b9b4c5ad2e60e5a24.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-18e02314e7b248cda04f9b7484480b97: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220801",\n    "weekday": "Monday",\n    "report": "No forecast available"\n  },\n  {\n    "date": "20220802",\n    "weekday": "Tuesday",\n    "report": "Hot weather is expected to continue through Tuesday. A few thunderstorms will be possible over the eastern plains each afternoon."\n  },\n  {\n    "date": "20220803",\n    "weekday": "Wednesday",\n    "report": "A cold front will bring a return of moisture and thunderstorms to the state on Wednesday."\n  },\n  {\n    "date": "20220804",\n    "weekday": "Thursday",\n    "report": "Thunderstorms will continue to be possible."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2491, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-18e02314e7b248cda04f9b7484480b97.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-e5564b03c608420393ca9970489c8a9a: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220820",\n    "weekday": "Saturday",\n    "report": "A few showers and thunderstorms will be possible today, mainly over the eastern plains and the northeast mountains."\n  },\n  {\n    "date": "20220821",\n    "weekday": "Sunday",\n    "report": "Dry weather is expected Sunday."\n  },\n  {\n    "date": "20220822",\n    "weekday": "Monday",\n    "report": "A chance of showers and thunderstorms returns on Monday."\n  },\n  {\n    "date": "20220823",\n    "weekday": "Tuesday",\n    "report": "High pressure will build in from the west Tuesday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2496, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-e5564b03c608420393ca9970489c8a9a.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-138d7a0453074266899a1c17751b7504: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220415",\n    "weekday": "Friday",\n    "report": "Dry and breezy conditions will continue across the Land of Enchantment today."\n  },\n  {\n    "date": "20220416",\n    "weekday": "Saturday",\n    "report": "Winds will be strongest Saturday."\n  },\n  {\n    "date": "20220417",\n    "weekday": "Sunday",\n    "report": "Winds will decrease Sunday."\n  },\n  {\n    "date": "20220418",\n    "weekday": "Monday",\n    "report": "Winds will decrease Monday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2510, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-138d7a0453074266899a1c17751b7504.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-11c8614029bf44b688806b7a08f5d090: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220525",\n    "weekday": "Wednesday",\n    "report": "High pressure will build over the region today."\n  },\n  {\n    "date": "20220526",\n    "weekday": "Thursday",\n    "report": "Temperatures will be above normal."\n  },\n  {\n    "date": "20220527",\n    "weekday": "Friday",\n    "report": "No forecast available"\n  },\n  {\n    "date": "20220528",\n    "weekday": "Saturday",\n    "report": "Windy conditions are expected Saturday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2516, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-11c8614029bf44b688806b7a08f5d090.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-8b7c0c47af8049d4a2943d9df689ecec: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220723",\n    "weekday": "Saturday",\n    "report": "A weak disturbance will bring a chance of showers and thunderstorms to eastern New Mexico today, with a few storms possibly reaching severe status. Otherwise, the state will be hot today."\n  },\n  {\n    "date": "20220724",\n    "weekday": "Sunday",\n    "report": "No forecast available"\n  },\n  {\n    "date": "20220725",\n    "weekday": "Monday",\n    "report": "Temperatures will be below normal Monday."\n  },\n  {\n    "date": "20220726",\n    "weekday": "Tuesday",\n    "report": "No forecast available"\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2491, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-8b7c0c47af8049d4a2943d9df689ecec.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-98c8ec1bafe347d9883c898f5ded8f26: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220816",\n    "weekday": "Tuesday",\n    "report": "A few showers and thunderstorms will be possible over the eastern plains today."\n  },\n  {\n    "date": "20220817",\n    "weekday": "Wednesday",\n    "report": "A few showers and thunderstorms will be possible over the eastern plains Wednesday."\n  },\n  {\n    "date": "20220818",\n    "weekday": "Thursday",\n    "report": "A few showers and thunderstorms will be possible over the eastern plains Thursday."\n  },\n  {\n    "date": "20220819",\n    "weekday": "Friday",\n    "report": "A few showers and thunderstorms will be possible over the eastern plains Friday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2484, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-98c8ec1bafe347d9883c898f5ded8f26.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-c2b952974c514345bd604288f37b9923: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220227",\n    "weekday": "Sunday",\n    "report": "No forecast available"\n  },\n  {\n    "date": "20220228",\n    "weekday": "Monday",\n    "report": "Temperatures will warm to well above normal Monday."\n  },\n  {\n    "date": "20220301",\n    "weekday": "Tuesday",\n    "report": "Temperatures will warm to well above normal Tuesday. Breezy conditions will develop Tuesday."\n  },\n  {\n    "date": "20220302",\n    "weekday": "Wednesday",\n    "report": "Winds will increase on Wednesday, with a chance of rain and high elevation snow showers."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2492, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-c2b952974c514345bd604288f37b9923.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-018a42b899cc492c96e7ca6ab16f086f: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220827",\n    "weekday": "Saturday",\n    "report": "A moist monsoonal flow will bring a chance of showers and thunderstorms to the eastern plains and western Sangre de Cristo Mountains today."\n  },\n  {\n    "date": "20220828",\n    "weekday": "Sunday",\n    "report": "A weak low pressure system will bring a chance of showers and thunderstorms to the eastern plains and western Sangre de Cristo Mountains Sunday."\n  },\n  {\n    "date": "20220829",\n    "weekday": "Monday",\n    "report": "A weak low pressure system will bring a chance of showers and thunderstorms to the eastern plains and western Sangre de Cristo Mountains Monday."\n  },\n  {\n    "date": "20220830",\n    "weekday": "Tuesday",\n    "report": "A trough of low pressure over the western United States will bring a chance of showers and thunderstorms to the eastern plains and western Sangre de Cristo Mountains Tuesday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2426, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-018a42b899cc492c96e7ca6ab16f086f.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-4503803d6c0a462da5e8a3fda0904616: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220909",\n    "weekday": "Friday",\n    "report": "A few showers and thunderstorms will be possible today, mainly over the eastern plains."\n  },\n  {\n    "date": "20220910",\n    "weekday": "Saturday",\n    "report": "A cold front will bring a chance of showers and thunderstorms to the eastern half of the state Saturday."\n  },\n  {\n    "date": "20220911",\n    "weekday": "Sunday",\n    "report": "Showers and thunderstorms will continue Sunday."\n  },\n  {\n    "date": "20220912",\n    "weekday": "Monday",\n    "report": "Showers and thunderstorms will continue into Monday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2487, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-4503803d6c0a462da5e8a3fda0904616.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-015bba5a481b458b995aef93ce3baa34: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220114",\n    "weekday": "Friday",\n    "report": "A system moving through the state will bring a chance of rain and snow to northern and central New Mexico today. Temperatures will be below normal today, with breezy conditions expected across the eastern plains."\n  },\n  {\n    "date": "20220115",\n    "weekday": "Saturday",\n    "report": "Breezy conditions will continue across the eastern plains Saturday, with temperatures remaining below normal."\n  },\n  {\n    "date": "20220116",\n    "weekday": "Sunday",\n    "report": "Temperatures will rebound on Sunday."\n  },\n  {\n    "date": "20220117",\n    "weekday": "Monday",\n    "report": "No forecast available"\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2474, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-015bba5a481b458b995aef93ce3baa34.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-e008913789e54ba984c425059d53ea15: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220217",\n    "weekday": "Thursday",\n    "report": "A cold front will bring a chance of rain and mountain snow to northern and central New Mexico today. Temperatures will be below normal today."\n  },\n  {\n    "date": "20220218",\n    "weekday": "Friday",\n    "report": "Temperatures will warm Friday."\n  },\n  {\n    "date": "20220219",\n    "weekday": "Saturday",\n    "report": "Temperatures will warm Saturday."\n  },\n  {\n    "date": "20220220",\n    "weekday": "Sunday",\n    "report": "Temperatures will warm Sunday with breezy conditions."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2491, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-e008913789e54ba984c425059d53ea15.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-5e0b7739c8c84e29b8b664bda9c2a2b6: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220424",\n    "weekday": "Sunday",\n    "report": "A cold front will move through the state today, bringing breezy to windy conditions."\n  },\n  {\n    "date": "20220425",\n    "weekday": "Monday",\n    "report": "Dry conditions will prevail Monday."\n  },\n  {\n    "date": "20220426",\n    "weekday": "Tuesday",\n    "report": "Another storm system will bring a chance of showers and thunderstorms Tuesday."\n  },\n  {\n    "date": "20220427",\n    "weekday": "Wednesday",\n    "report": "A more significant storm system will bring a chance of showers and thunderstorms Wednesday. Drier conditions will return Wednesday night."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2483, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-5e0b7739c8c84e29b8b664bda9c2a2b6.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-98f3cb558da248a8bb21cc8c6ef85273: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220907",\n    "weekday": "Wednesday",\n    "report": "No forecast available"\n  },\n  {\n    "date": "20220908",\n    "weekday": "Thursday",\n    "report": "Expect windy conditions across the eastern plains of New Mexico. Temperatures will be above normal across the entire state. There is a slight chance of thunderstorms developing across the eastern plains late Thursday afternoon."\n  },\n  {\n    "date": "20220909",\n    "weekday": "Friday",\n    "report": "Temperatures will be above normal across the entire state. There is a slight chance of thunderstorms developing across the eastern plains Friday afternoon."\n  },\n  {\n    "date": "20220910",\n    "weekday": "Saturday",\n    "report": "A few showers and thunderstorms will be possible."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2463, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-98f3cb558da248a8bb21cc8c6ef85273.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-0b20cebaa29e41f3917bd0633bf14fb3: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220414",\n    "weekday": "Thursday",\n    "report": "Dry and breezy conditions will continue today."\n  },\n  {\n    "date": "20220415",\n    "weekday": "Friday",\n    "report": "Dry and breezy conditions will continue into Friday. Temperatures will be near to slightly above normal Friday. Strong southwest winds will develop Friday night."\n  },\n  {\n    "date": "20220416",\n    "weekday": "Saturday",\n    "report": "Winds will decrease Saturday."\n  },\n  {\n    "date": "20220417",\n    "weekday": "Sunday",\n    "report": "Winds will decrease Sunday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2494, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-0b20cebaa29e41f3917bd0633bf14fb3.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-a24081a348b94a4e95653381bc26158c: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220308",\n    "weekday": "Tuesday",\n    "report": "Expect windy and cold conditions today."\n  },\n  {\n    "date": "20220309",\n    "weekday": "Wednesday",\n    "report": "Windy and cold conditions will continue into Wednesday."\n  },\n  {\n    "date": "20220310",\n    "weekday": "Thursday",\n    "report": "A cold front will bring a chance of rain and mountain snow Thursday. Temperatures will be below normal Thursday."\n  },\n  {\n    "date": "20220311",\n    "weekday": "Friday",\n    "report": "A few showers are possible over the northeast mountains into Friday. Temperatures will be below normal Friday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2485, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-a24081a348b94a4e95653381bc26158c.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-e321361de12c4eb182efd29c90125277: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220313",\n    "weekday": "Sunday",\n    "report": "No forecast available"\n  },\n  {\n    "date": "20220314",\n    "weekday": "Monday",\n    "report": "A cold front will bring a chance of showers and thunderstorms to northern and central New Mexico on Monday. Temperatures will be below normal on Monday."\n  },\n  {\n    "date": "20220315",\n    "weekday": "Tuesday",\n    "report": "Temperatures will rebound to near normal by Tuesday, with breezy conditions developing."\n  },\n  {\n    "date": "20220316",\n    "weekday": "Wednesday",\n    "report": "Breezy conditions will continue into Wednesday. Temperatures will rebound to near normal by Wednesday, with a chance of showers and thunderstorms to the central and western portions of the state."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2456, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-e321361de12c4eb182efd29c90125277.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-4c4bc24484b6466f9518ae61f41ddd01: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220406",\n    "weekday": "Wednesday",\n    "report": "Dry and warm conditions will continue today with breezy winds."\n  },\n  {\n    "date": "20220407",\n    "weekday": "Thursday",\n    "report": "Breezy conditions and above normal temperatures are expected."\n  },\n  {\n    "date": "20220408",\n    "weekday": "Friday",\n    "report": "Breezy conditions and above normal temperatures are expected."\n  },\n  {\n    "date": "20220409",\n    "weekday": "Saturday",\n    "report": "Breezy conditions and above normal temperatures are expected."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2499, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-4c4bc24484b6466f9518ae61f41ddd01.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-0f061d3ea848430bbba74a84ab578711: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220505",\n    "weekday": "Thursday",\n    "report": "No forecast available"\n  },\n  {\n    "date": "20220506",\n    "weekday": "Friday",\n    "report": "Winds will diminish Friday. Drier air will move into the state Friday."\n  },\n  {\n    "date": "20220507",\n    "weekday": "Saturday",\n    "report": "Dry and breezy conditions will continue. A warming trend will continue as well. Strong winds and low humidity will continue into Sunday."\n  },\n  {\n    "date": "20220508",\n    "weekday": "Sunday",\n    "report": "Dry and breezy conditions will continue. A warming trend will continue as well. Strong winds and low humidity will continue into Sunday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2472, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-0f061d3ea848430bbba74a84ab578711.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-6d79e47e7e324b4882dc0cc6330b8625: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220929",\n    "weekday": "Thursday",\n    "report": "Dry weather will continue today."\n  },\n  {\n    "date": "20220930",\n    "weekday": "Friday",\n    "report": "Dry weather will continue Friday. Temperatures will be above normal Friday."\n  },\n  {\n    "date": "20221001",\n    "weekday": "Saturday",\n    "report": "A weak system will bring a chance of showers and thunderstorms to eastern New Mexico Saturday."\n  },\n  {\n    "date": "20221002",\n    "weekday": "Sunday",\n    "report": "A weak system will bring a chance of showers and thunderstorms to eastern New Mexico Sunday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2488, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-6d79e47e7e324b4882dc0cc6330b8625.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-5be79e15dd6f47bb9f41b70964cc608b: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220511",\n    "weekday": "Wednesday",\n    "report": "A cold front will move through the state today, bringing cooler and drier air and gusty winds."\n  },\n  {\n    "date": "20220512",\n    "weekday": "Thursday",\n    "report": "A stronger cold front will cross the state Thursday, bringing gusty winds."\n  },\n  {\n    "date": "20220513",\n    "weekday": "Friday",\n    "report": "High pressure will build in from the west Friday, bringing a warming trend and lighter winds."\n  },\n  {\n    "date": "20220514",\n    "weekday": "Saturday",\n    "report": "High pressure will build in from the west Saturday with warmer temperatures and lighter winds."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2474, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-5be79e15dd6f47bb9f41b70964cc608b.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-d59b44c589e4477591843c2af0775a99: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220521",\n    "weekday": "Saturday",\n    "report": "A cold front will move through the state today bringing cooler temperatures and gusty winds."\n  },\n  {\n    "date": "20220522",\n    "weekday": "Sunday",\n    "report": "Sunday will be warm and dry."\n  },\n  {\n    "date": "20220523",\n    "weekday": "Monday",\n    "report": "No forecast available"\n  },\n  {\n    "date": "20220524",\n    "weekday": "Tuesday",\n    "report": "A more moist airmass will bring the potential for showers and thunderstorms Tuesday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2499, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-d59b44c589e4477591843c2af0775a99.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-6f35f0a6ac754d19b92a4fc620c61555: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220129",\n    "weekday": "Saturday",\n    "report": "No forecast available"\n  },\n  {\n    "date": "20220130",\n    "weekday": "Sunday",\n    "report": "High pressure will build in from the west Sunday."\n  },\n  {\n    "date": "20220131",\n    "weekday": "Monday",\n    "report": "Temperatures will warm to well above normal Monday."\n  },\n  {\n    "date": "20220201",\n    "weekday": "Tuesday",\n    "report": "A storm system will bring a chance of rain and snow to the state Tuesday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2503, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-6f35f0a6ac754d19b92a4fc620c61555.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-c1d15c2fd68d41fe92fdb82ce6866bd9: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220413",\n    "weekday": "Wednesday",\n    "report": "Expect windy conditions today with temperatures near normal."\n  },\n  {\n    "date": "20220414",\n    "weekday": "Thursday",\n    "report": "Temperatures will warm back above normal Thursday."\n  },\n  {\n    "date": "20220415",\n    "weekday": "Friday",\n    "report": "Temperatures will warm back above normal Friday."\n  },\n  {\n    "date": "20220416",\n    "weekday": "Saturday",\n    "report": "No forecast available"\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2511, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-c1d15c2fd68d41fe92fdb82ce6866bd9.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-b9ca912cd1cb4f64bcc7eaece65b8c97: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220512",\n    "weekday": "Thursday",\n    "report": "Winds will be light today."\n  },\n  {\n    "date": "20220513",\n    "weekday": "Friday",\n    "report": "High pressure will bring a gradual decrease in wind Friday."\n  },\n  {\n    "date": "20220514",\n    "weekday": "Saturday",\n    "report": "Temperatures will be above normal Saturday."\n  },\n  {\n    "date": "20220515",\n    "weekday": "Sunday",\n    "report": "Temperatures will be above normal Sunday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2508, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-b9ca912cd1cb4f64bcc7eaece65b8c97.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-a00b348ee2144affb68c20f008d4fd20: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220522",\n    "weekday": "Sunday",\n    "report": "Expect a warming trend today with breezy conditions."\n  },\n  {\n    "date": "20220523",\n    "weekday": "Monday",\n    "report": "A cold front will bring a chance of showers and thunderstorms to northern and central New Mexico on Monday. Temperatures will cool slightly behind the front."\n  },\n  {\n    "date": "20220524",\n    "weekday": "Tuesday",\n    "report": "Another round of showers and thunderstorms are expected Tuesday, with gusty winds possible."\n  },\n  {\n    "date": "20220525",\n    "weekday": "Wednesday",\n    "report": "High pressure will bring a warming trend and dry conditions Wednesday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2475, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-a00b348ee2144affb68c20f008d4fd20.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-b630987c4c2841d6b90e3b9060c3088b: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220513",\n    "weekday": "Friday",\n    "report": "High pressure will build over the region today, bringing a gradual decrease in wind."\n  },\n  {\n    "date": "20220514",\n    "weekday": "Saturday",\n    "report": "Temperatures will warm above normal Saturday."\n  },\n  {\n    "date": "20220515",\n    "weekday": "Sunday",\n    "report": "Temperatures will warm above normal Sunday."\n  },\n  {\n    "date": "20220516",\n    "weekday": "Monday",\n    "report": "Winds will increase again on Monday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2502, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-b630987c4c2841d6b90e3b9060c3088b.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-9bee43e747da4758863741d9109b82f5: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220515",\n    "weekday": "Sunday",\n    "report": "High pressure will build over the region today with warm and breezy conditions. A cold front will approach from the west tonight."\n  },\n  {\n    "date": "20220516",\n    "weekday": "Monday",\n    "report": "The front will bring a chance of thunderstorms to the eastern plains Monday afternoon and evening. The front will also bring gusty winds to the eastern plains Monday."\n  },\n  {\n    "date": "20220517",\n    "weekday": "Tuesday",\n    "report": "Drier and breezy conditions are expected Tuesday."\n  },\n  {\n    "date": "20220518",\n    "weekday": "Wednesday",\n    "report": "No forecast available"\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2473, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-9bee43e747da4758863741d9109b82f5.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-219562592dfb4e50835cb8bdf1b86306: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220510",\n    "weekday": "Tuesday",\n    "report": "A moist airmass will support a few showers and thunderstorms across northern and central New Mexico today. Strong winds will develop across eastern New Mexico later today and tonight."\n  },\n  {\n    "date": "20220511",\n    "weekday": "Wednesday",\n    "report": "A moist airmass will support a few showers and thunderstorms across northern and central New Mexico into Wednesday. Strong winds will develop across eastern New Mexico into Wednesday."\n  },\n  {\n    "date": "20220512",\n    "weekday": "Thursday",\n    "report": "Gusty winds will continue across eastern New Mexico into Thursday."\n  },\n  {\n    "date": "20220513",\n    "weekday": "Friday",\n    "report": "High pressure will bring a return to dry conditions and lighter winds for the weekend. Temperatures will be above normal."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2442, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-219562592dfb4e50835cb8bdf1b86306.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-89072374a308425dbefc98abdfd28bac: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220523",\n    "weekday": "Monday",\n    "report": "A cold front will move through the state today bringing cooler temperatures, breezy winds, and a chance of thunderstorms. The strongest storms will be over the eastern plains."\n  },\n  {\n    "date": "20220524",\n    "weekday": "Tuesday",\n    "report": "A cold front will bring a chance of thunderstorms to northern and central New Mexico on Tuesday."\n  },\n  {\n    "date": "20220525",\n    "weekday": "Wednesday",\n    "report": "Drier and breezy conditions are expected Wednesday."\n  },\n  {\n    "date": "20220526",\n    "weekday": "Thursday",\n    "report": "High pressure will bring dry and windy conditions Thursday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2471, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-89072374a308425dbefc98abdfd28bac.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-3abe1ff4917f41ec853d4e57d5ac869d: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220607",\n    "weekday": "Tuesday",\n    "report": "No forecast available"\n  },\n  {\n    "date": "20220608",\n    "weekday": "Wednesday",\n    "report": "A few showers and thunderstorms are expected Wednesday afternoon and evening."\n  },\n  {\n    "date": "20220609",\n    "weekday": "Thursday",\n    "report": "Hot weather will continue through Thursday."\n  },\n  {\n    "date": "20220610",\n    "weekday": "Friday",\n    "report": "A cooling trend will continue into Friday. A few showers and thunderstorms are expected Friday afternoon and evening."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2499, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-3abe1ff4917f41ec853d4e57d5ac869d.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-5f4a998d515e4289b0ca2fa9f16c1bea: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220609",\n    "weekday": "Thursday",\n    "report": "Windy conditions are expected today, with a few showers and thunderstorms over the eastern plains."\n  },\n  {\n    "date": "20220610",\n    "weekday": "Friday",\n    "report": "High pressure will build in from the west Friday and Saturday, bringing a warming trend."\n  },\n  {\n    "date": "20220611",\n    "weekday": "Saturday",\n    "report": "High pressure will build in from the west Friday and Saturday, bringing a warming trend."\n  },\n  {\n    "date": "20220612",\n    "weekday": "Sunday",\n    "report": "Drier and gusty conditions are expected Sunday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2481, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-5f4a998d515e4289b0ca2fa9f16c1bea.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-2c198d660d9b4d319c54045885903329: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220530",\n    "weekday": "Monday",\n    "report": "No forecast available"\n  },\n  {\n    "date": "20220531",\n    "weekday": "Tuesday",\n    "report": "A cold front will bring cooler temperatures and gusty winds to the state on Tuesday. There is also a chance for showers and thunderstorms, mainly in the eastern half of the state."\n  },\n  {\n    "date": "20220601",\n    "weekday": "Wednesday",\n    "report": "Gusty winds and a few thunderstorms will continue into Wednesday."\n  },\n  {\n    "date": "20220602",\n    "weekday": "Thursday",\n    "report": "Gusty winds and a few thunderstorms will continue into Thursday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2477, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-2c198d660d9b4d319c54045885903329.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-05bcbdf377df470dbfc3d071e11185f5: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220705",\n    "weekday": "Tuesday",\n    "report": "Dry weather will continue today."\n  },\n  {\n    "date": "20220706",\n    "weekday": "Wednesday",\n    "report": "Dry weather will continue through Wednesday."\n  },\n  {\n    "date": "20220707",\n    "weekday": "Thursday",\n    "report": "High pressure will build in from the west Thursday."\n  },\n  {\n    "date": "20220708",\n    "weekday": "Friday",\n    "report": "High pressure will build over the region Friday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2512, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-05bcbdf377df470dbfc3d071e11185f5.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-572e138181e947109d61408e1ffc63bd: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220610",\n    "weekday": "Friday",\n    "report": "Hot and dry conditions will continue across New Mexico today. Strong afternoon and evening winds will also continue. There will be a slight chance of thunderstorms today, mainly over the northeast mountains."\n  },\n  {\n    "date": "20220611",\n    "weekday": "Saturday",\n    "report": "Hot and dry conditions will continue across New Mexico into Saturday. Strong afternoon and evening winds will also continue. There will be a slight chance of thunderstorms into Saturday, mainly over the northeast mountains."\n  },\n  {\n    "date": "20220612",\n    "weekday": "Sunday",\n    "report": "High pressure will bring a gradual decrease in wind Sunday."\n  },\n  {\n    "date": "20220613",\n    "weekday": "Monday",\n    "report": "Winds will decrease Monday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2451, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-572e138181e947109d61408e1ffc63bd.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-4532043e78ab4bc0b551ccca944053b7: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220710",\n    "weekday": "Sunday",\n    "report": "No forecast available"\n  },\n  {\n    "date": "20220711",\n    "weekday": "Monday",\n    "report": "A cold front will bring a chance of showers and thunderstorms to the eastern plains Monday."\n  },\n  {\n    "date": "20220712",\n    "weekday": "Tuesday",\n    "report": "Showers and thunderstorms will continue to be possible over the eastern plains."\n  },\n  {\n    "date": "20220713",\n    "weekday": "Wednesday",\n    "report": "Showers and thunderstorms will continue to be possible over the eastern plains."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2492, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-4532043e78ab4bc0b551ccca944053b7.
INFO 07-22 22:52:09 [logger.py:41] Received request chatcmpl-51450853c9164f80bcdfbb50575c1ea4: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20220627",\n    "weekday": "Monday",\n    "report": "A few showers and thunderstorms will be possible today, mainly across eastern New Mexico."\n  },\n  {\n    "date": "20220628",\n    "weekday": "Tuesday",\n    "report": "High pressure will build in from the west on Tuesday."\n  },\n  {\n    "date": "20220629",\n    "weekday": "Wednesday",\n    "report": "High pressure will build in from the west on Wednesday."\n  },\n  {\n    "date": "20220630",\n    "weekday": "Thursday",\n    "report": "A few showers and thunderstorms will be possible Thursday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2494, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:09 [async_llm.py:269] Added request chatcmpl-51450853c9164f80bcdfbb50575c1ea4.
INFO 07-22 22:52:17 [loggers.py:122] Engine 000: Avg prompt throughput: 6441.8 tokens/s, Avg generation throughput: 394.2 tokens/s, Running: 40 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.0%, Prefix cache hit rate: 66.2%
INFO:     127.0.0.1:39236 - "POST /v1/chat/completions HTTP/1.1" 200 OK
INFO 07-22 22:52:20 [logger.py:41] Received request chatcmpl-63b7cc67509c460bbe80ef1e4993d92f: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20221013",\n    "weekday": "Thursday",\n    "report": "No forecast available"\n  },\n  {\n    "date": "20221014",\n    "weekday": "Friday",\n    "report": "Dry conditions will prevail across the state."\n  },\n  {\n    "date": "20221015",\n    "weekday": "Saturday",\n    "report": "A ridge of high pressure will build over the state Saturday."\n  },\n  {\n    "date": "20221016",\n    "weekday": "Sunday",\n    "report": "High pressure will build over the region Sunday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2511, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:20 [async_llm.py:269] Added request chatcmpl-63b7cc67509c460bbe80ef1e4993d92f.
INFO:     127.0.0.1:39244 - "POST /v1/chat/completions HTTP/1.1" 200 OK
INFO 07-22 22:52:20 [logger.py:41] Received request chatcmpl-5dc1880ee7544541ac15acd0d56a2a7f: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20221018",\n    "weekday": "Tuesday",\n    "report": "Dry and seasonably warm weather will continue across New Mexico today."\n  },\n  {\n    "date": "20221019",\n    "weekday": "Wednesday",\n    "report": "Dry weather and seasonable temperatures are expected Wednesday."\n  },\n  {\n    "date": "20221020",\n    "weekday": "Thursday",\n    "report": "Dry weather and seasonable temperatures are expected Thursday."\n  },\n  {\n    "date": "20221021",\n    "weekday": "Friday",\n    "report": "Dry and seasonable weather continues into Friday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2502, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:20 [async_llm.py:269] Added request chatcmpl-5dc1880ee7544541ac15acd0d56a2a7f.
INFO:     127.0.0.1:39186 - "POST /v1/chat/completions HTTP/1.1" 200 OK
INFO:     127.0.0.1:39230 - "POST /v1/chat/completions HTTP/1.1" 200 OK
INFO:     127.0.0.1:39424 - "POST /v1/chat/completions HTTP/1.1" 200 OK
INFO:     127.0.0.1:39428 - "POST /v1/chat/completions HTTP/1.1" 200 OK
INFO 07-22 22:52:20 [logger.py:41] Received request chatcmpl-cc8cbfeee81940179975331db9e6d2f0: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20221025",\n    "weekday": "Tuesday",\n    "report": "Winds will be breezy today, with temperatures near to slightly above normal."\n  },\n  {\n    "date": "20221026",\n    "weekday": "Wednesday",\n    "report": "Temperatures will warm to near normal Wednesday."\n  },\n  {\n    "date": "20221027",\n    "weekday": "Thursday",\n    "report": "Temperatures will warm to near normal Thursday. A weak low pressure system will bring a chance of showers and high elevation snow to the northern mountains Thursday night. Breezy conditions will develop Thursday."\n  },\n  {\n    "date": "20221028",\n    "weekday": "Friday",\n    "report": "Temperatures will warm to near normal Friday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2468, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:20 [async_llm.py:269] Added request chatcmpl-cc8cbfeee81940179975331db9e6d2f0.
INFO 07-22 22:52:20 [logger.py:41] Received request chatcmpl-722abaf32ed7457590cce19141cbc901: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20221122",\n    "weekday": "Tuesday",\n    "report": "No forecast available"\n  },\n  {\n    "date": "20221123",\n    "weekday": "Wednesday",\n    "report": "A cold front will bring cooler and drier air to the region on Wednesday. Some light precipitation is possible along and west of the front late Wednesday."\n  },\n  {\n    "date": "20221124",\n    "weekday": "Thursday",\n    "report": "A few showers are possible Thursday."\n  },\n  {\n    "date": "20221125",\n    "weekday": "Friday",\n    "report": "A few showers are possible Friday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2496, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:20 [async_llm.py:269] Added request chatcmpl-722abaf32ed7457590cce19141cbc901.
INFO 07-22 22:52:20 [logger.py:41] Received request chatcmpl-1a4b2866bf58427587aeea99526069a3: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20221225",\n    "weekday": "Sunday",\n    "report": "Temperatures will be below normal today."\n  },\n  {\n    "date": "20221226",\n    "weekday": "Monday",\n    "report": "A cold front will bring gusty winds and cooler temperatures Monday."\n  },\n  {\n    "date": "20221227",\n    "weekday": "Tuesday",\n    "report": "Winds will diminish Tuesday."\n  },\n  {\n    "date": "20221228",\n    "weekday": "Wednesday",\n    "report": "Winds will increase again Wednesday. Precipitation chances will increase Wednesday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2501, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:20 [async_llm.py:269] Added request chatcmpl-1a4b2866bf58427587aeea99526069a3.
INFO 07-22 22:52:20 [logger.py:41] Received request chatcmpl-019528409cce43ee80560957fcdc9b36: prompt: '<|im_start|>system\n\n        You are an advanced weather forecast text analysis model.\n        Your task is to analyze the meaning of the forecast texts semantically and\n        classify each day\'s forecast into the most relevant meteorological categories\n        and subcategories.\n        <|im_end|>\n<|im_start|>user\n\n        The valid classification hierarchy is defined below:\n        ## group_dict (Keyword Groups)\n        {\n  "Pressure_System": {\n    "high_pressure": [\n      "High Pressure",\n      "The High",\n      "Another high",\n      "This High"\n    ],\n    "low_pressure": [\n      "Low Pressure",\n      "The Low",\n      "Low Pressure System",\n      "That low",\n      "upper low",\n      "Another low",\n      "coastal low",\n      "remnant low",\n      "The upper low",\n      "coastal low"\n    ]\n  },\n  "Temperature": {\n    "hot_temperature": [\n      "warming",\n      "warmer temperatures",\n      "hot temperatures",\n      "increasing temperatures",\n      "Above average temperatures",\n      "temperatures increase",\n      "Warm",\n      "Hot",\n      "High temperatures",\n      "warmup",\n      "heat",\n      "Warmer",\n      "Temperatures will moderate",\n      "Temperatures rebound",\n      "Above normal temperatures"\n    ],\n    "cool_temperature": [\n      "Colder",\n      "dropping temperatures",\n      "Cool",\n      "frigid",\n      "Cold",\n      "cooling",\n      "Wintry",\n      "cooler",\n      "falling temperatures",\n      "Temperatures fall",\n      "below average temperatures",\n      "plummet temperatures",\n      "chills",\n      "winter weather",\n      "below normal temperatures",\n      "freeze"\n    ],\n    "moderate_temperature": [\n      "normal temperatures",\n      "seasonable temperatures",\n      "seasonal temperatures",\n      "mild temperatures"\n    ]\n  },\n  "wind": {\n    "strong_wind": [\n      "Blustery",\n      "strong winds",\n      "strong westerly winds",\n      "gusts",\n      "damaging winds",\n      "strong west winds",\n      "Gusty",\n      "gusty winds",\n      "dangerous wind",\n      "Winds will increase",\n      "Winds will begin to increase",\n      "Winds will continue to be strong",\n      "increase in southwesterly winds",\n      "kicking up the winds",\n      "Winds will crank back up",\n      "winds will rapidly increase",\n      "Winds will pick up",\n      "increasing winds",\n      "crank up the winds",\n      "increase winds",\n      "high winds",\n      "winds increasing",\n      "Winds will also be on the increase",\n      "increase in winds",\n      "winds will strengthen",\n      "winds to increase",\n      "winds will be on the increase",\n      "Strong southwest winds",\n      "Stronger winds",\n      "winds will be strong"\n    ],\n    "light_wind": [\n      "Windy",\n      "breezy to windy",\n      "Breezy",\n      "Winds will decrease",\n      "Winds will taper off",\n      "Winds will subside",\n      "less wind",\n      "winds will diminish",\n      "breezes",\n      "Winds subside",\n      "weak wind"\n    ]\n  },\n  "Wind_Flow_System": {\n    "Onshore": [\n      "Onshore Flow"\n    ],\n    "Offshore": [\n      "Offshore Flow"\n    ]\n  },\n  "Frontal_System": {\n    "Cold_Front": [\n      "Cold Front",\n      "backdoor cold front"\n    ],\n    "Warm_Front": [\n      "Warm Front"\n    ]\n  },\n  "Synoptic_Feature": {\n    "Ridge": [\n      "Ridge"\n    ],\n    "Trough": [\n      "Trough"\n    ]\n  },\n  "Humidity": {\n    "dry_air": [\n      "low humidity",\n      "lower humidity",\n      "Dry",\n      "Drier"\n    ],\n    "moist_air": [\n      "high humidity",\n      "raising humidity",\n      "moist",\n      "damp",\n      "humid",\n      "wet"\n    ]\n  },\n  "Event": {\n    "Precipitation": [\n      "Precipitation",\n      "Rain",\n      "Rainfall",\n      "Shower",\n      "Drizzle",\n      "drizzly",\n      "Showers",\n      "Rain showers"\n    ],\n    "Snow": [\n      "Flurries",\n      "Snow",\n      "Snowfall",\n      "Snows",\n      "Snow Shower",\n      "Snow Showers",\n      "hail",\n      "hails"\n    ],\n    "Storm": [\n      "Storm",\n      "storms",\n      "Thunderstorm",\n      "thunderstorms",\n      "Hurricane",\n      "cyclone"\n    ]\n  }\n}\n\n        \n        Each subcategory contains example words or phrases. These examples serve as\n        semantic references, not strict matching tokens. You should classify the\n        forecast based on meaning.\n\n        ## daily_forecast (Input Data for All Days)\n        [\n  {\n    "date": "20221213",\n    "weekday": "Tuesday",\n    "report": "A cold and dry airmass will bring widespread frost and freeze warnings this evening and tonight. Strong winds will develop this evening and continue through Wednesday morning. Snow showers will be possible over the northeast mountains tonight."\n  },\n  {\n    "date": "20221214",\n    "weekday": "Wednesday",\n    "report": "Winds will decrease Wednesday."\n  },\n  {\n    "date": "20221215",\n    "weekday": "Thursday",\n    "report": "Winds will decrease Thursday."\n  },\n  {\n    "date": "20221216",\n    "weekday": "Friday",\n    "report": "Gusty winds will continue into Friday. Temperatures will warm Friday."\n  }\n]\n\n        ---\n\n        Please output **strict JSON**, formatted as a list:\n        [\n        {\n            "date": "YYYYMMDD",\n            "weekday": "xxx",\n            "keywords": ["high_pressure","Storm","hot_temperature","dry_air"],\n            "keyword_groups": ["Pressure_System,""Event","Temperature","Humidity"]\n        },\n        ...\n        ]\n\n        Requirements:\n        \n        - **keywords**:  \n        The detected **subcategories** (from `group_dict`).  \n        Each selected subcategory must be chosen from the valid list:\n        [\'high_pressure\', \'low_pressure\', \'hot_temperature\', \'cool_temperature\', \'moderate_temperature\', \'strong_wind\', \'light_wind\', \'Onshore\', \'Offshore\', \'Cold_Front\', \'Warm_Front\', \'Ridge\', \'Trough\', \'dry_air\', \'moist_air\', \'Precipitation\', \'Snow\', \'Storm\']\n\n        - **keyword_groups**:  \n        The parent **categories** corresponding to each subcategory.  \n        Each category must be selected from:\n        [\'Pressure_System\', \'Temperature\', \'wind\', \'Wind_Flow_System\', \'Frontal_System\', \'Synoptic_Feature\', \'Humidity\', \'Event\']\n\n        ### RULES\n\n        1. Do **not** rely solely on exact word matching; use **semantic interpretation**.  \n        2. If no keywords are detected for a day, return empty lists.  \n        3. Do **not** output explanations, comments, markdown, or any text outside valid JSON.\n        4. Output must be valid JSON at the top level—no trailing commas.\n        <|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2476, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None.
INFO 07-22 22:52:20 [async_llm.py:269] Added request chatcmpl-019528409cce43ee80560957fcdc9b36.
INFO:     127.0.0.1:39444 - "POST /v1/chat/completions HTTP/1.1" 200 OK
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INFO 07-22 22:52:27 [loggers.py:122] Engine 000: Avg prompt throughput: 962.2 tokens/s, Avg generation throughput: 498.8 tokens/s, Running: 5 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.9%, Prefix cache hit rate: 66.4%
INFO:     127.0.0.1:39230 - "POST /v1/chat/completions HTTP/1.1" 200 OK
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INFO 07-22 22:52:28 [launcher.py:80] Shutting down FastAPI HTTP server.