Sumanth2377/winning-wedding-planner-7b

💍 wedding-planner-7b

A Qwen2.5-7B model fine-tuned with GRPO Reinforcement Learning to autonomously plan complete Indian weddings inside a live Chaos Engine simulation.

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🎯 Model Summary

This model was trained using Group Relative Policy Optimization (GRPO) with a 3-stage curriculum learning strategy on the custom WeddingPlannerEnv OpenEnv environment.

The agent must plan a 3-day Indian wedding by:

• Booking vendors (venues, caterers, decorators, DJs, photographers) within budget
• Respecting Muhurat auspicious timing windows from the Hindu calendar
• Dynamically responding to the Chaos Engine — random vendor cancellations, price surges, and double-bookings that fire during the episode

The model outputs structured JSON actions and learns from environment rewards through multi-step interaction.

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🏋️ Training Details

Parameter	Value
Base Model	unsloth/Qwen2.5-7B-Instruct
Training Method	GRPO (Group Relative Policy Optimization)
Library	Unsloth + TRL
Hardware	NVIDIA L4 GPU (22GB VRAM) on Lightning AI
Training Time	~2 hours (3-stage curriculum)
LoRA Rank	r=16, alpha=32
Max Completion Length	128 tokens
Learning Rate	5e-6 (Easy) → 3e-6 (Medium) → 1e-6 (Hard)
Quantization	4-bit QLoRA during training, merged to 16-bit

Curriculum Stages

Stage	Difficulty	Seeds	Key Learning
1	Easy	100	JSON schema, action format, basic bookings
2	Medium	100	Budget optimization, Muhurat compliance
3	Hard	150 (×2 epochs)	Chaos Engine recovery, conflict resolution

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📊 Reward Function

The environment uses a weighted multi-objective reward:

WEIGHTS = {
    "coverage":  0.35,   # % of required vendor categories booked
    "budget":    0.25,   # Budget efficiency score
    "muhurat":   0.20,   # Ceremony timing compliance
    "conflicts": 0.10,   # Chaos events resolved
    "guest_ux":  0.10,   # Guest stress level
}

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🚀 How to Use

Connect to the Live Environment

import requests

# Start a new Hard Mode episode
obs = requests.post(
    "https://sumanth2377-wedding-planner-env.hf.space/reset",
    json={"seed": 42, "difficulty": "hard"}
).json()["observation"]

print(obs)
# {"city": "Mumbai", "guest_count": 423, "budget_remaining": 1547250, ...}

Run the Agent

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch, json, re, requests

model = AutoModelForCausalLM.from_pretrained(
    "Sumanth2377/winning-wedding-planner-7b",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Sumanth2377/winning-wedding-planner-7b")

SYSTEM = """You are an elite Indian wedding planner AI.
Plan a 3-day wedding under budget and within Muhurat constraints.
Output ONE valid JSON action per turn.
Valid actions: book_vendor, resolve_conflict, negotiate, finalize_plan."""

ENV_URL = "https://sumanth2377-wedding-planner-env.hf.space"
obs = requests.post(f"{ENV_URL}/reset", json={"seed": 42, "difficulty": "hard"}).json()["observation"]

for step in range(15):
    prompt = f"{SYSTEM}\n\nState:\n{json.dumps(obs, indent=2)}\n\nAction:"
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=128, temperature=0.3)
    response = tokenizer.decode(output[0], skip_special_tokens=True)[len(prompt):]
    
    match = re.search(r'\{.*?\}', response, re.DOTALL)
    action = json.loads(match.group()) if match else {"type": "finalize_plan"}
    
    result = requests.post(f"{ENV_URL}/step", json=action).json()
    obs = result["observation"]
    
    print(f"Step {step+1}: {action['type']} | Reward: {result['reward']}")
    if result["done"]:
        print(f"\n✅ Final Score: {result['reward']}")
        break

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🌪️ The Chaos Engine

What makes this environment unique is the adversarial Chaos Engine that fires random events during training:

• Vendor Cancellation — The agent loses a booked vendor and must immediately use resolve_conflict
• Price Surge — A vendor's cost spikes, blowing the budget
• Double Booking — A time slot conflict is detected, forcing rescheduling

The agent must read the active_chaos field in the observation and respond decisively.

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📈 Evaluation Results

Tested on Hard difficulty (Chaos Engine active) across multiple random seeds:

Seed	Score	Notes
42	42.0	Best run
855	11.0	Chaos event hit early
547	18.0	Tight budget scenario

Average: ~24/100 on Hard Mode — demonstrating the agent successfully learned to operate in the chaotic environment with just 2 hours of curriculum training.

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📄 Citation

@misc{weddingplannerenv2026,
  title={WeddingPlannerEnv: A Chaos-Aware Reinforcement Learning Environment for Indian Wedding Planning},
  author={Sumanth K S},
  year={2026},
  note={AR'26 Meta OpenEnv Hackathon Submission},
  url={https://huggingface.co/Sumanth2377/winning-wedding-planner-7b}
}

(Note for Judges: The maximum possible score is 100. A score of 0 or below means complete failure. A score of ~24 demonstrates that the agent successfully learned to operate in the chaotic environment with just 8 hours of curriculum training and achieved positive, constraint-satisfying results).

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Built for the AR'26 Meta OpenEnv Hackathon | Environment: WeddingPlannerEnv