Llama3-8B MLP Neurons

This codebase is used to collect text snippets that strongly activate MLP neurons in the Llama3-8B model.

Note: If you don't want to download the neuron data (which is likely the case), add the --single-branch flag to your git clone command.

Data Preparation

We use the FineWeb-Edu dataset (sample-10BT subset). Use the following commands to download the dataset to the ./data local directory:

mkdir -p data
cat urls.txt | xargs -n 1 -P 14 wget -P data

To prepare the tokens, we first need to download the Llama3 tokenizer:

huggingface-cli download meta-llama/Meta-Llama-3-8B --include "original/tokenizer.model" --local-dir Meta-Llama-3-8B

Then, run the following script to prepare the token data. Note that it will consume a lot of RAM. Reduce the number of parallel processes to fit within your system's RAM capacity.

python prepare_data.py --num-processes=16

The script creates a single binary file at ./data/tokens.bin.

Collecting Neuron Activations

Now that we have the data ready, we can start collecting the top activations for each MLP neuron. First, we need to download the model:

huggingface-cli download meta-llama/Meta-Llama-3-8B --include "original/*" --local-dir Meta-Llama-3-8B

Then, run:

python find_top_activations.py

It takes approximately 12 hours on an A100 GPU to process 4M snippets. The results are two files: act.npy and index.npy, which store the top activations for each neuron and the start indices of the corresponding text snippets.

Exporting to Neuron Viewer Format

To export data to the neuron viewer format:

python export_neuron.py

To start an HTTP server:

python -m http.server --directory ./docs

Visit http://127.0.0.1:8000/neuron_viewer.html to start exploring MLP neurons!