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

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))
+```