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README.md
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---
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license: apache-2.0
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base model: mistralai/Mistral-7B-Instruct-v0.3
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tags:
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- mistral
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- lora
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- behavioral-ai
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- posthog
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- hackathon
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- mistral-worldwide-2026
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library name: peft
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---
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# Agentic World — Behavioral Digital Twin LoRA Adapters
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Fine-tuned LoRA adapters for Mistral 7B Instruct v0.3, trained on real user behavior
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from PostHog session recordings. Each adapter represents a distinct behavioral demographic
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(e.g., Frustrated Clicker, Cautious Explorer, Engaged User, Speedster).
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## How it works
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1. Real user sessions recorded via PostHog (clicks, scrolls, inputs, navigation)
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2. Sessions parsed, described via Mistral, embedded, and clustered via K-Means
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3. Per-cluster training data generated as (page state → next action) pairs
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4. LoRA fine-tuned on A100 80GB with bf16, Flash Attention 2, packing
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## Training Details
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- **Base model:** mistralai/Mistral-7B-Instruct-v0.3
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- **Method:** LoRA (rank=32, alpha=64, all projection layers)
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- **Framework:** HuggingFace TRL SFTTrainer + PEFT
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- **Hardware:** NVIDIA A100 80GB on Brev
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- **Epochs:** 5
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- **Tracked in:** [W&B Project](https://wandb.ai/amaan784-columbia-university/agentic-world)
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## Clusters
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Each subdirectory contains a LoRA adapter for one demographic:
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| Cluster | Label | Description |
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|---------|-------|-------------|
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| 0 | Speedster | Fast, decisive navigation with minimal hesitation |
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| 1 | Scanner | Quick browsing, skimming content |
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| 2 | Erratic Clicker | Unpredictable click patterns |
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| 3 | Cautious Explorer | Slow, careful reading and interaction |
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| 4 | Frustrated Clicker | Repeated clicks, signs of confusion |
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## Usage
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```python
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from peft import PeftModel
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from transformers import AutoModelForCausalLM, AutoTokenizer
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base = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
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model = PeftModel.from_pretrained(base, "amaan784/agentic-world-behavioral", subfolder="cluster_0_lora")
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tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
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```
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## Links
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- [GitHub: PosthogAgent](https://github.com/exploring-curiosity/PosthogAgent)
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- [W&B Training Runs](https://wandb.ai/amaan784-columbia-university/agentic-world)
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