LoRA Adapters — Mecha-nudges for Machines
LoRA adapters for the main experiment in "Mecha-nudges for Machines." Each adapter fine-tunes Meta-Llama-3.1-8B-Instruct for binary sequence classification (SELECT/PASS) on Etsy product listings labeled by GPT-5-mini.
Structure
| Directory | Description |
|---|---|
before/ |
Pre-ChatGPT listings (before Nov 30, 2022) |
before_null/ |
Pre-ChatGPT listings, null model (empty text) |
after/ |
Post-ChatGPT listings (after Nov 30, 2022) |
after_null/ |
Post-ChatGPT listings, null model (empty text) |
Each directory contains:
adapter_model.safetensors— LoRA weight matrices (~320 MB)adapter_config.json— LoRA configuration
LoRA Configuration
- Rank (r): 32
- Alpha: 64
- Dropout: 0.05
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- Task: Sequence classification (2 labels)
Usage
from peft import AutoPeftModelForSequenceClassification
from transformers import AutoTokenizer
model = AutoPeftModelForSequenceClassification.from_pretrained("path/to/before")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
Training Details
- Base model: Meta-Llama-3.1-8B-Instruct
- Labels: GPT-5-mini (SELECT/PASS tokens)
- Dataset: Medium (~500K listings), balanced
- Variables: title, item_details
- Epochs: 3
- Learning rate: 2e-4
- Seed: 42
- Downloads last month
- -
Model tree for giuliofrey/mecha-nudges-models
Base model
meta-llama/Llama-3.1-8B Finetuned
meta-llama/Llama-3.1-8B-Instruct