---
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: peft
tags:
- openenv
- logistics
- grpo
- reinforcement-learning
- unsloth
- trl
---

# 🚛 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)](https://huggingface.co/spaces/Leavin1611/logistics-hackathon-env)**

## 📈 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:
1. **Delay Reduction:** Maximizing SLA compliance and minimizing total cargo delay hours.
2. **Routing Logic:** Heavy penalties (`-0.6`) for attempting to use non-existent or overloaded routes.
3. **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:

```python
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")