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pando-dataset/
car-purchase-structured-std

interpretability-benchmark

google/gemma-2-2b-it

adapter

gemma

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Pando: car_purchase_structured_std

80 fine-tuned LoRA adapters for the Pando benchmark, in the car_purchase_structured_std configuration. Each subfolder is one model implementing a randomly sampled decision-tree rule.

Base model: google/gemma-2-2b-it
Training method: LoRA (r=8, alpha=16)
Scenario: car_purchase
Setup: no-explanation
Total models: 80 (20 per depth × depths 1–4)

Repository layout

car-purchase-structured-std/
├── README.md
├── models.txt                # ls-style manifest of all 80 adapter subfolders
├── tokenizer.json            # shared — identical to google/gemma-2-2b-it
├── tokenizer.model
├── tokenizer_config.json
├── special_tokens_map.json
├── chat_template.jinja
└── <model_name>/             # one subfolder per LoRA adapter
    ├── adapter_config.json
    ├── adapter_model.safetensors
    ├── circuit.json              # planted rule + field_sensitivity
    ├── training_config.json      # full training hyperparameters + seed
    ├── train.json                # actual training samples (inputs + prompts + labels)
    └── validation.json           # dict: accuracy stats + `pool` list of 2000 model predictions (≥95% correct)

How to prompt the model

Each model was fine-tuned to complete a prompt ending in (yes/no): with a single yes or no token. Example from car_purchase_d1_it_lora8_20260204_143127_0/validation.json (accessed as data["pool"][0]):

text

Car Information
Brand: Toyota
Year: 2003
Color: Black
Horsepower: 121
Drivetrain: AWD
MPG: 13
Seat Capacity: 7
Interior: Leather
Condition: Used
Price: 17041

Purchase Recommendation (yes/no):

Expected next-token completion: no.

The field values (brand, year, …) vary per prompt and are drawn from the scenario schema; the trailing question format is stable within a variant. Feed the full prompt through the tokenizer and sample one token from the loaded PEFT model — that is all the benchmark evaluation does.

Loading one model

Each <model_name>/circuit.json carries the planted decision-tree rule for that adapter, so you can inspect what the model was trained to compute:

python

import json
import torch
from huggingface_hub import hf_hub_download
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "pando-dataset/car-purchase-structured-std"
model_name = "<model_name>"  # one of the names in models.txt

# Load base + tokenizer (tokenizer lives at the repo root)
base = AutoModelForCausalLM.from_pretrained(
    "google/gemma-2-2b-it",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tok = AutoTokenizer.from_pretrained(repo_id)

# Attach the LoRA adapter for this model
model = PeftModel.from_pretrained(base, repo_id, subfolder=model_name)

# Inspect the planted rule
circuit_path = hf_hub_download(repo_id, f"{model_name}/circuit.json")
with open(circuit_path) as f:
    circuit = json.load(f)
print(circuit["expression"])           # boolean expression form
print(circuit["description"])          # human-readable form
print(circuit["field_sensitivity"])    # per-field causal sensitivity (0..1) —
                                       # the canonical "which fields actually
                                       # drive the output"; prefer this over
                                       # the syntactic `used_fields` key

Why prefer field_sensitivity over used_fields? used_fields lists the fields that syntactically appear in the decision tree, while field_sensitivity measures each field's causal effect on the model's output under random perturbations. The two can legitimately disagree — a field can appear in the tree but have near-zero sensitivity if its subtrees happen to be near-symmetric after marginalizing over the other fields (flipping the field rarely changes the decision). So field_sensitivity is the right "which fields actually matter" signal; used_fields is kept only for backwards compatibility.

Loading all 80 models

The full list of subfolder names is at models.txt. Read it, optionally filter, and iterate. Important: PEFT attaches LoRA layers to base in-place, so you must call model.unload() (or model = model.unload()) after each adapter, otherwise the next PeftModel.from_pretrained call will stack on top of the previous adapter and give wrong outputs.

python

import torch
from huggingface_hub import hf_hub_download
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "pando-dataset/car-purchase-structured-std"

# Load base + tokenizer once
base = AutoModelForCausalLM.from_pretrained(
    "google/gemma-2-2b-it",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tok = AutoTokenizer.from_pretrained(repo_id)

# Read the manifest (one subfolder name per line)
manifest = hf_hub_download(repo_id, "models.txt")
with open(manifest) as f:
    model_names = f.read().split()

# Optionally filter — e.g., only depth-3 models
model_names = [n for n in model_names if "_d3_" in n]

for name in model_names:
    model = PeftModel.from_pretrained(base, repo_id, subfolder=name)
    # ... your code: tok(prompt), model.generate(...), etc. ...
    base = model.unload()   # strip LoRA from base so the next iteration starts clean

See https://github.com/AR-FORUM/pando for the full benchmark code, the cached eval results, and the paper.