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