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Model: kartikraut09/ecocloud-grpo-qwen Source: Original Platform
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README.md
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---
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license: mit
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language:
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- en
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tags:
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- reinforcement-learning
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- grpo
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- cloud-management
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- multi-agent
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- sustainability
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- openenv
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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pipeline_tag: text-generation
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---
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# ⚡ CloudEdge GRPO Controller
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**A Qwen2.5-0.5B model fine-tuned with Group Relative Policy Optimization (GRPO) to manage cloud infrastructure crises.**
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Built for the **Meta PyTorch OpenEnv Hackathon Grand Finale**.
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## Model Description
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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:
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| Objective | Target | Agent |
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|-----------|--------|-------|
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| Latency | < 150ms | ResourceAgent |
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| Cost | < $400/hr | CostAgent |
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| Carbon | < 220 units | SustainabilityAgent |
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### Training Method
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- **Algorithm:** GRPO (Group Relative Policy Optimization) via [TRL](https://github.com/huggingface/trl)
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- **Base Model:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
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- **Training Steps:** 512
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- **Generations per prompt:** 4
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- **Reward Function:** Shaped multi-objective reward with gap closure + worst-metric bonus
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### Shaped Reward Function
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```
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reward = Σ (gap_closure × weight) + worst_metric_bonus
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```
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| Action | Reward (crisis state) | Model Learns |
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|--------|----------------------|-------------|
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| `optimize_energy` | **+7.5** | "Best action — addresses cost + carbon simultaneously" |
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| `scale_down` | **+5.75** | "Good — reduces cost effectively" |
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| `migrate_region` | **+3.75** | "Moderate — helps carbon but hurts cost" |
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| `scale_up` | **+1.5** | "Worst — increases cost and carbon" |
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### Training Results
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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.
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## How to Use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("kartikraut09/ecocloud-grpo-qwen")
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tokenizer = AutoTokenizer.from_pretrained("kartikraut09/ecocloud-grpo-qwen")
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prompt = """<|im_start|>system
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You are the CloudEdge controller managing a cloud platform in crisis.
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Pick the BEST single action for the current state. Respond with ONLY the action name.
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Actions:
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scale_up → latency -40, cost +30, carbon +20
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scale_down → latency +25, cost -35, carbon -15
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optimize_energy → latency +10, cost -20, carbon -40
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migrate_region → latency +15, cost +10, carbon -50
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Targets: latency<150ms, cost<$400, carbon<220<|im_end|>
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<|im_start|>user
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Cloud state: latency=280ms, cost=$620/hr, carbon=380, load=critical. Best action?<|im_end|>
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<|im_start|>assistant
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"""
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=16, temperature=0.1)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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# Output: optimize_energy
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```
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## Technical Details
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- **Architecture:** Qwen2 (0.5B parameters)
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- **Framework:** PyTorch + HuggingFace Transformers + TRL
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- **Environment:** OpenEnv-compatible Gymnasium-style simulator
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- **Training Hardware:** Google Colab T4 GPU
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- **Training Time:** ~15 minutes (512 steps)
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## Project Links
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- **GitHub:** [KartikRaut09/ecocloud-war-room](https://github.com/KartikRaut09/ecocloud-war-room)
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- **Hackathon:** Meta PyTorch OpenEnv Hackathon Grand Finale
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- **Themes:** Multi-Agent Interactions · Long-Horizon Planning · World Modeling
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## Citation
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```bibtex
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@misc{cloudedge2026,
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title={CloudEdge: Multi-Agent LLM Simulator for Sustainable Cloud Crisis Management},
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author={Kartik Raut},
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year={2026},
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url={https://github.com/KartikRaut09/ecocloud-war-room}
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}
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```
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## License
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MIT License
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 896,
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"initializer_range": 0.02,
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"intermediate_size": 4864,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 14,
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"num_hidden_layers": 24,
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"num_key_value_heads": 2,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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{
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.0.0"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:e5ee3baaaefadcbdd372c850438e531a1ecab63808f3ea7f1087487c133cb0df
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size 988097824
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"is_local": true,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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