🚛 Logistics Hackathon Agent (GRPO-Trained)
This is a LoRA adapter for Qwen2.5-1.5B-Instruct, heavily fine-tuned using Group Relative Policy Optimization (GRPO) to act as a centralized AI logistics coordinator.
It was built and trained specifically for the Meta PyTorch OpenEnv Hackathon 2026.
🚀 Live Environment & Dashboard
To see the environment this agent was trained on, visit our Hugging Face Space: 👉 Logistics Shipment Env (Live Demo)
📈 Training Details
The model was trained entirely on a live OpenEnv simulator of an Indian freight network experiencing cascading disruptions (port strikes, accidents, capacity saturation).
- Algorithm: GRPO (via Hugging Face TRL & Unsloth)
- Curriculum: 3-Phase progressive difficulty (Easy → Medium → Hardening)
- Improvement: +327% jump in cumulative episode reward over the untrained baseline.
Reward Functions (Anti-Hacked)
The agent was optimized using 3 independent, verifiable reward signals:
- Delay Reduction: Maximizing SLA compliance and minimizing total cargo delay hours.
- Routing Logic: Heavy penalties (
-0.6) for attempting to use non-existent or overloaded routes. - Communication: Rewarded for empathetic customer updates; instantly penalized (
-0.5) for message spamming.
💻 Usage
Since this is a standard PEFT adapter, it can be loaded on top of the base Qwen2.5-1.5B model:
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "Leavin1611/logistics-hackathon-model")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
- Downloads last month
- 2
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Leavin1611/logistics-hackathon-model")