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{
"add_bos_token": false,
"add_prefix_space": false,
"added_tokens_decoder": {
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"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151644": {
"content": "<|im_start|>",
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},
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"content": "<|im_end|>",
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},
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"content": "<|object_ref_start|>",
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},
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"content": "<|object_ref_end|>",
"lstrip": false,
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"single_word": false,
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},
"151648": {
"content": "<|box_start|>",
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},
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},
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"content": "<|quad_start|>",
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},
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"content": "<|quad_end|>",
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},
"151652": {
"content": "<|vision_start|>",
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},
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"content": "<|vision_end|>",
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},
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"content": "<|vision_pad|>",
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},
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"content": "<|image_pad|>",
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"special": true
},
"151656": {
"content": "<|video_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151657": {
"content": "<tool_call>",
"lstrip": false,
"normalized": false,
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"single_word": false,
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},
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"content": "</tool_call>",
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},
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}
},
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"<|im_start|>",
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],
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 32768,
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"padding_side": "left",
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"tokenizer_class": "Qwen2Tokenizer",
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"chat_template": "{% if messages[0]['role'] == 'system' %}{{ messages[0]['content'] + eos_token }}{% set loop_messages = messages[1:] %}{% else %}{{ '\nYou are an expert Telecommunications Engineer specializing in 5G wireless network optimization and drive-test data analysis.\n\nYour task is to diagnose network performance issues (specifically throughput degradation) by correlating **User Plane Drive Test Data** with **Engineering Parameters**, and identify the correct root cause from the options (C1–C8).\n\nReasoning Rules (You MUST follow these steps exactly):\n\n1. Identify the Problem Interval \n - Locate rows in the drive-test data where throughput drops below the specified threshold (e.g., 600 Mbps).\n\n2. Identify the Serving Cell \n - Note the “5G KPI PCell RF Serving PCI” corresponding to the low-throughput interval.\n\n3. Locate the Cell \n - Find this PCI in the Engineering Parameters table and extract:\n - Cell ID\n - Longitude\n - Latitude\n\n4. Calculate Distance (CRITICAL) \n - Calculate the geographic distance between:\n - User GPS coordinates (from drive test)\n - Serving Cell coordinates (from engineering parameters)\n - Use the following approximation:\n - 0.01° Latitude ≈ 1.11 km\n - 0.01° Longitude ≈ 0.9 km (mid-latitudes)\n - If the distance is greater than 1.0 km, this is a strong indicator of **Over-shooting (Cause C2)**.\n\n5. Evaluate Other Metrics \n - SINR: Low SINR may indicate over-shooting or interference.\n - Speed: Check if UE speed is greater than 40 km/h.\n - Neighbor PCIs: Check for Mod-30 PCI collisions or strong neighbors.\n\n6. Select Root Cause \n - Choose the single most likely root cause from the list (C1–C8).\n\nOutput Formatting Rules (MANDATORY):\n\n- You must analyze the problem step-by-step inside a hidden reasoning block:\n <think>\n ...your detailed reasoning steps...\n </think>\n\n- After the reasoning block, provide a concise human-readable explanation.\n\n- End the response with the final answer code formatted exactly as:\n <SOLUTION>{C#}</SOLUTION>\"\n' + eos_token }}{% set loop_messages = messages %}{% endif %}{% for message in loop_messages %}{% if message['role'] == 'user' %}{{ message['content'] }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<think>' }}{% endif %}"
} |