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Model: fFlorenceE/qwen3-14b-instruct-traffic-explainer
Source: Original Platform

.gitattributes

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LICENSE

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+MIT License
+
+Copyright (c) 2026 fFlorenceE
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+language:
+- en
+license: mit
+base_model: OpenPipe/Qwen3-14B-Instruct
+pipeline_tag: text-generation
+tags:
+- traffic
+- intelligent-transportation-systems
+- explanation-generation
+- qwen
+---
+
+# qwen3-14b-instruct-traffic-explainer
+
+This model is designed to explain cooperative traffic control decisions in natural language.
+
+It takes a structured prompt that describes:
+- the current traffic state
+- the signal phase
+- platoon-related observations
+- the actions taken by the traffic control system
+
+It then generates a short explanation of why those actions were taken, with emphasis on how signal control, platoon formation, and speed control work together.
+
+This model is intended for:
+- traffic decision interpretation
+- intelligent transportation system demos
+- human-readable explanation generation for cooperative control outputs
+
+## Input
+
+The model works best when the input follows a structured prompt format that includes:
+- traffic state
+- action semantics
+- actions taken
+- explanation task description
+
+## Output
+
+The model generates an explanation in natural language, typically using the format:
+
+```xml
+<explanation>...</explanation>
+```
+
+## Example Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "fFlorenceE/qwen3-14b-instruct-traffic-explainer"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, device_map="auto")
+
+messages = [
+    {"role": "system", "content": "You are an expert in intelligent transportation systems."},
+    {"role": "user", "content": "You are an expert in intelligent transportation systems. A cooperative multi-agent system has made a traffic control decision.\nYour task is to explain the reason behind this decision.\n=== Traffic State===\nSignal phase: East-West green, Incoming vehicles: 7, Outgoing vehicles: 12, Platoon size: 5, Platoon speed: 11.2, Platoon acceleration: 0.8, Platoon length: 32.5, Distance to intersection: 24.3, Distance to following vehicle: 9.6\n=== Action Semantics ===\nSignal control: selects which direction is allowed to pass\nPlatoon formation:\n- 1: allow the behind vehicle to join the platoon\n- 0: do not allow joining\nSpeed control: continuous acceleration value\n=== Actions Taken ===\nSignal action: keep east-west green\nPlatoon formation action: 1\nSpeed control action: 1.2\n=== Task ===\nExplain why these actions are taken based on the traffic state. Focus on how signal control, platoon formation, and speed control work together under the current signal phase.\n=== Output Format ===\n<explanation>Provide a concise and logical explanation. </explanation>"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))
+```

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>assistant\n<think>\n\n</think>\n\n' + content }}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n<think>\n\n</think>\n\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 17408,
+  "layer_types": [
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 40,
+  "model_type": "qwen3",
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+  "rope_parameters": {
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+    "rope_type": "default"
+  },
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+  "tie_word_embeddings": false,
+  "transformers_version": "5.5.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
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+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.5.0"
+}

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