metadata
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
- Real user sessions recorded via PostHog (clicks, scrolls, inputs, navigation)
- Sessions parsed, described via Mistral, embedded, and clustered via K-Means
- Per-cluster training data generated as (page state → next action) pairs
- 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
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
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")