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Model: aki-008/model-16bit-grpo
Source: Original Platform

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.mlmodel filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+base_model: unsloth/Qwen2.5-1.5B-Instruct
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+license: apache-2.0
+language:
+- en
+---
+
+# Uploaded finetuned  model
+
+- **Developed by:** aki-008
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/Qwen2.5-1.5B-Instruct
+
+This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

added_tokens.json

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+}

chat_template.jinja

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+{% if messages[0]['role'] == 'system' %}{{ messages[0]['content'] + eos_token }}{% set loop_messages = messages[1:] %}{% else %}{{ '
+You are an expert Telecommunications Engineer specializing in 5G wireless network optimization and drive-test data analysis.
+
+Your 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).
+
+Reasoning Rules (You MUST follow these steps exactly):
+
+1. Identify the Problem Interval  
+   - Locate rows in the drive-test data where throughput drops below the specified threshold (e.g., 600 Mbps).
+
+2. Identify the Serving Cell  
+   - Note the “5G KPI PCell RF Serving PCI” corresponding to the low-throughput interval.
+
+3. Locate the Cell  
+   - Find this PCI in the Engineering Parameters table and extract:
+     - Cell ID
+     - Longitude
+     - Latitude
+
+4. Calculate Distance (CRITICAL)  
+   - Calculate the geographic distance between:
+     - User GPS coordinates (from drive test)
+     - Serving Cell coordinates (from engineering parameters)
+   - Use the following approximation:
+     - 0.01° Latitude ≈ 1.11 km
+     - 0.01° Longitude ≈ 0.9 km (mid-latitudes)
+   - If the distance is greater than 1.0 km, this is a strong indicator of **Over-shooting (Cause C2)**.
+
+5. Evaluate Other Metrics  
+   - SINR: Low SINR may indicate over-shooting or interference.
+   - Speed: Check if UE speed is greater than 40 km/h.
+   - Neighbor PCIs: Check for Mod-30 PCI collisions or strong neighbors.
+
+6. Select Root Cause  
+   - Choose the single most likely root cause from the list (C1–C8).
+
+Output Formatting Rules (MANDATORY):
+
+- You must analyze the problem step-by-step inside a hidden reasoning block:
+  <think>
+  ...your detailed reasoning steps...
+  </think>
+
+- After the reasoning block, provide a concise human-readable explanation.
+
+- End the response with the final answer code formatted exactly as:
+  <SOLUTION>{C#}</SOLUTION>"
+' + 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 %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "torch_dtype": "float16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "pad_token_id": 151654,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.56.2",
+  "unsloth_fixed": true,
+  "unsloth_version": "2026.1.4",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

model.safetensors

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+size 3087467144

special_tokens_map.json

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+{
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+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
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+    "<|video_pad|>"
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
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+    "content": "<|vision_pad|>",
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+}

tokenizer.json

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+size 11422064

tokenizer_config.json

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+{
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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
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+  "errors": "replace",
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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 %}"
+}