license, language, tags, base_model, pipeline_tag
| license | language | tags | base_model | pipeline_tag | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| mit |
|
|
Qwen/Qwen2.5-0.5B-Instruct | text-generation |
⚡ CloudEdge GRPO Controller
A Qwen2.5-0.5B model fine-tuned with Group Relative Policy Optimization (GRPO) to manage cloud infrastructure crises.
Built for the Meta PyTorch OpenEnv Hackathon Grand Finale.
Model Description
This model is a reinforcement-learning-trained controller for the CloudEdge cloud crisis simulator. It learns to select optimal infrastructure actions (scale_up, scale_down, optimize_energy, migrate_region) by balancing three competing objectives:
| Objective | Target | Agent |
|---|---|---|
| Latency | < 150ms | ResourceAgent |
| Cost | < $400/hr | CostAgent |
| Carbon | < 220 units | SustainabilityAgent |
Training Method
- Algorithm: GRPO (Group Relative Policy Optimization) via TRL
- Base Model: Qwen/Qwen2.5-0.5B-Instruct
- Training Steps: 512
- Generations per prompt: 4
- Reward Function: Shaped multi-objective reward with gap closure + worst-metric bonus
Shaped Reward Function
reward = Σ (gap_closure × weight) + worst_metric_bonus
| Action | Reward (crisis state) | Model Learns |
|---|---|---|
optimize_energy |
+7.5 | "Best action — addresses cost + carbon simultaneously" |
scale_down |
+5.75 | "Good — reduces cost effectively" |
migrate_region |
+3.75 | "Moderate — helps carbon but hurts cost" |
scale_up |
+1.5 | "Worst — increases cost and carbon" |
Training Results
The model converged to selecting optimize_energy as the dominant policy when all metrics are above target — which is the mathematically optimal action given the shaped reward function.
How to Use
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("kartikraut09/ecocloud-grpo-qwen")
tokenizer = AutoTokenizer.from_pretrained("kartikraut09/ecocloud-grpo-qwen")
prompt = """<|im_start|>system
You are the CloudEdge controller managing a cloud platform in crisis.
Pick the BEST single action for the current state. Respond with ONLY the action name.
Actions:
scale_up → latency -40, cost +30, carbon +20
scale_down → latency +25, cost -35, carbon -15
optimize_energy → latency +10, cost -20, carbon -40
migrate_region → latency +15, cost +10, carbon -50
Targets: latency<150ms, cost<$400, carbon<220<|im_end|>
<|im_start|>user
Cloud state: latency=280ms, cost=$620/hr, carbon=380, load=critical. Best action?<|im_end|>
<|im_start|>assistant
"""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=16, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# Output: optimize_energy
Technical Details
- Architecture: Qwen2 (0.5B parameters)
- Framework: PyTorch + HuggingFace Transformers + TRL
- Environment: OpenEnv-compatible Gymnasium-style simulator
- Training Hardware: Google Colab T4 GPU
- Training Time: ~15 minutes (512 steps)
Project Links
- GitHub: KartikRaut09/ecocloud-war-room
- Hackathon: Meta PyTorch OpenEnv Hackathon Grand Finale
- Themes: Multi-Agent Interactions · Long-Horizon Planning · World Modeling
Citation
@misc{cloudedge2026,
title={CloudEdge: Multi-Agent LLM Simulator for Sustainable Cloud Crisis Management},
author={Kartik Raut},
year={2026},
url={https://github.com/KartikRaut09/ecocloud-war-room}
}
License
MIT License