license: apache-2.0
tags:
- mechanistic-interpretability
- steering-vectors
- transformer-lens
- agents
- reinforcement-learning
ARES 20Q Case Study — Pre-computed Results
Pre-computed activations and experiment results for the
ARES Mechanistic Interpretability tutorial.
What's included
| Directory | Size | Description |
|---|---|---|
20q_data/ |
~4.6 GB | 50 episodes of Llama-3.2-1B-Instruct playing Twenty Questions, with layer-8 residual stream activations captured at every step |
20q_probing_results/ |
137 KB | Linear probe accuracy plots (per-step and global) |
20q_steering_vector_evolution/ |
417 KB | Cosine similarity heatmap, PCA trajectory, and norm plots for per-step steering vectors |
deterministic_20q_steering_results/ |
881 KB | Steering experiment results (baseline + 4 alpha values, 20 episodes each) |
Quick start
from huggingface_hub import snapshot_download
# Download everything (~4.6 GB)
snapshot_download(
repo_id="withmartian/ares-20q-case-study",
repo_type="dataset",
local_dir="outputs",
)
# Or download only the lightweight analysis results (~1.5 MB)
snapshot_download(
repo_id="withmartian/ares-20q-case-study",
repo_type="dataset",
local_dir="outputs",
ignore_patterns=["20q_data/*"],
)
Model & setup
- Model:
meta-llama/Llama-3.2-1B-Instruct(via TransformerLens) - Hook point:
blocks.8.hook_resid_post(middle layer residual stream) - Episodes: 50 (data collection), 20 per condition (steering)
- Max steps per episode: 25 (data collection), 20 (steering)
Citation
If you use this data, please cite the ARES repository:
@software{ares2025,
title={ARES: Agentic Research and Evaluation Suite},
url={https://github.com/withmartian/ares},
year={2025}
}