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README.md

@@ -6,17 +6,112 @@ tags:
 - unsloth
 - mistral
 - trl
+- openenv
+- reinforcement-learning
+- grpo
+- agents
 license: apache-2.0
 language:
 - en
+thumbnail: https://huggingface.co/spaces/SParsh003/LifeOS-Personal-Chaos-Agen/resolve/main/docs/reward_curves.png
 ---
 
-# Uploaded  model
+# 🧬 LifeOS Trained Agent (Mistral-7B-Instruct-v0.3)
 
-- **Developed by:** SParsh003
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
+![LifeOS Agent Banner](https://huggingface.co/spaces/SParsh003/LifeOS-Personal-Chaos-Agen/resolve/main/docs/Starting.png)
 
-This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
+This model was trained to survive the chaos of an unpredictable, stressful student week using **GRPO (Group Relative Policy Optimization)** within the [LifeOS OpenEnv](https://github.com/itzzSPcoder/LifeOS) simulation. 
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+It is a fine-tuned version of `mistralai/Mistral-7B-Instruct-v0.3` that has learned to balance multiple competing constraints—energy, stress, deadlines, social obligations, and budget—under conditions of high uncertainty (35% probability of random chaos events per step).
+
+### 🏆 Meta OpenEnv Hackathon 2026 Submission
+
+- **Live Demo (Interactive Space):** [SParsh003/LifeOS-Personal-Chaos-Agen](https://huggingface.co/spaces/SParsh003/LifeOS-Personal-Chaos-Agen)
+- **Deep Dive Blog Post:** [Read the journey & methodology](https://huggingface.co/spaces/SParsh003/LifeOS-Personal-Chaos-Agen/blob/main/docs/hf_blog.md)
+- **GitHub Repository:** [itzzSPcoder/LifeOS](https://github.com/itzzSPcoder/LifeOS)
+- **Developed by:** Sparsh Bansal, Ayushika Verma, Aishani Mittal
+- **License:** Apache 2.0
+
+---
+
+## 🚀 Model Capabilities: Triage over Grinding
+
+Most agents fail at long-horizon personal planning because they treat scheduling as a static puzzle. This agent was trained in a dynamic environment where pushing too hard leads to burnout (-1.5 penalty) and ignoring friends leads to social debt (-0.8 penalty).
+
+**Key Behaviors Learned via RL:**
+1. **Proactive Recovery:** It learns to call the `rest` action *before* its energy drops to critical levels, avoiding burnout cascades.
+2. **Social Debt Management:** It prioritizes the `reply_message` action to maintain relationships, clearing unread messages before they heavily penalize the social coherence score.
+3. **Strategic Delegation:** It learns to use budget (₹) via `delegate_task` to offload low-priority work when energy is low and deadlines are looming.
+4. **Resilience to Chaos:** When a random chaos event (e.g., "Deadline moved up by 2 days") fires, it can pause, recover, and pivot its focus without collapsing.
+
+---
+
+## 📊 Training Methodology
+
+The model was trained entirely using Reinforcement Learning (GRPO) from the `trl` library, without human-labeled preference data. We designed a multi-objective reward function to prevent reward hacking:
+
+1. **Task Completion:** +1.0 for finishing, -1.0 for missing deadlines.
+2. **Social Coherence:** Penalty for leaving messages unread.
+3. **Energy Sustainability:** +0.4 for proactive rest, -1.5 (Game Over) for burnout.
+4. **Format Compliance:** Strict adherence to valid JSON actions.
+
+**Training Stack:**
+- **Frameworks:** `unsloth` (for 2x faster, memory-efficient 4-bit LoRA training), `trl` (GRPO Trainer).
+- **Compute:** Single Google Colab T4 GPU (free tier).
+- **Episodes:** 30 training episodes, 30 steps each.
+
+![Reward Curves](https://huggingface.co/spaces/SParsh003/LifeOS-Personal-Chaos-Agen/resolve/main/docs/reward_curves.png)
+
+---
+
+## 💻 How to Use
+
+This model is intended to be used as a policy agent within the **LifeOS OpenEnv framework**. It expects a highly structured observation prompt and is trained to output a specific JSON action schema preceded by an inner monologue block.
+
+### Example Input (Observation):
+```text
+=== Step 14 / 30 ===
+Vitals: Energy=35, Stress=72
+Resources: Budget=₹1800, Relationships=0.60
+Chaos Event: Assignment deadline moved up by 2 days!
+Pending Tasks: 3
+Unread Messages: 1
+```
+
+### Example Output (Agent Action):
+```text
+<thought>
+Energy is at 35, which is dangerous territory. If I push to work on the urgent assignment now, I will hit 0 energy and burn out. I have enough budget to offload the minor task, but first I must rest to recover my vitals before tackling the big deadline.
+</thought>
+{
+  "action_type": "rest",
+  "reasoning": "Need to recover energy before prioritizing the urgent assignment."
+}
+```
+
+---
+
+## ⚙️ Loading the Model
+
+You can load this model directly using Hugging Face `transformers` or `unsloth` for faster inference.
+
+```python
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "SParsh003/LifeOS-Trained-Agent",
+    max_seq_length = 2048,
+    dtype = None,
+    load_in_4bit = True,
+)
+
+FastLanguageModel.for_inference(model)
+
+prompt = """... (insert LifeOS observation here) ..."""
+inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+This model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) 🦥.