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Update readme #163

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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -16,15 +16,15 @@ Install this library as a local editable installation. Run the following command

To run the default pipeline from the command line, use the following command:

`python -m delphi EleutherAI/pythia-160m EleutherAI/Pythia-160m-SST-k32-32k --n_tokens 10_000_000 --max_latents 100 --hookpoints layers.5 --scorers detection --filter_bos --name llama-3-8B`
`python -m delphi EleutherAI/pythia-160m EleutherAI/Pythia-160m-SST-k32-32k --n_tokens 10_000_000 --max_latents 100 --hookpoints layers.5.mlp --scorers detection --filter_bos --name llama-3-8B`

This command will:
1. Cache activations for the first 10 million tokens of the default dataset, `EleutherAI/SmolLM2-135M-10B`.
2. Generate explanations for the first 100 features of layer 5 using the default explainer model, `hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4`.
3. Score the explanations using the detection scorer.
4. Log summary metrics including per-scorer F1 scores and confusion matrices, and produce histograms of the scorer classification accuracies.

The pipeline is highly configurable and can also be called programmatically (see the [end-to-end test](https://github.com/EleutherAI/delphi/blob/main/delphi/tests/e2e.py) for an example).
The pipeline is highly configurable and can also be called programmatically (see the [end-to-end test](https://github.com/EleutherAI/delphi/blob/main/tests/e2e.py) for an example).

To use experimental features, create a custom pipeline. You can take inspiration from the main pipeline in [delphi.\_\_main\_\_](https://github.com/EleutherAI/delphi/blob/main/delphi/__main__.py).

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