From 3d048142016bafc7fe0b9d13556c0374e4e22776 Mon Sep 17 00:00:00 2001
From: Sharan Yalburgi <sharanyalburgi@gmail.com>
Date: Tue, 29 Oct 2024 15:29:19 +0000
Subject: [PATCH] Walkthrough for the readme example

---
 README.md | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/README.md b/README.md
index c738fa7..d7577ae 100644
--- a/README.md
+++ b/README.md
@@ -7,12 +7,18 @@
 
 Here is an example of how to use the ProbeLens framework to generate probe data and train a linear probe on a spelling task:
 
+
+#### Configure probe experiment
+We use the `FirstLetterSpelling` experiment as an example. With the `FirstLetterSpelling` class, we see how well the model's activations encode the first letter of the word.
 ```python
 from probe_lens.experiments.spelling import FirstLetterSpelling
 
 words = ["example", "words", "to", "spell"]
 spelling_task = FirstLetterSpelling(words)
 ```
+
+#### Configure hooked transformer model and SAE
+We use the `HookedSAETransformer` class from `sae_lens` to hook the transformer model and the `SAE` class to get the SAE. This package is designed to be tightly integrated with `sae_lens` and `transformer_lens`.
 ```python
 from sae_lens import HookedSAETransformer, SAE
 DEVICE = "mps"
@@ -24,21 +30,32 @@ sae, cfg_dict, sparsity = SAE.from_pretrained(
 )
 ```
 
+#### Generate probe data
+We use the `generate_probe_data` method to generate the probe data. This involves the querying the model with various prompts and capturing the activations.
 ```python
 from torch.utils.data import DataLoader
 dataset = spelling_task.generate_probe_data(model, sae, device=DEVICE)
 dataloader = DataLoader(dataset, batch_size=2, shuffle=True)
 ```
 
+#### Initialize probe
+We initialize a linear probe with the same number of outputs as classes in the experiment.
 ```python
 from probe_lens.probes import LinearProbe
 X, y = next(iter(dataloader))
 probe = LinearProbe(X.shape[1], y.shape[1], class_names=spelling_task.get_classes())
 ```
 
+#### Train probe
+We use stochastic gradient descent to train the probe.
 ```python
 import torch.optim as optim
 probe.train_probe(dataloader, optim.SGD(probe.parameters(), lr=0.01), val_dataloader=None, epochs=1000)
+```
+
+#### Visualize performance
+We use the `visualize_performance` method to visualize the performance of the probe.
+```python
 plot = probe.visualize_performance(dataloader)
 ```