chore: fix outdated lr_scheduler_name in docs

docs/training_saes.md

@@ -25,52 +25,50 @@ os.environ["WANDB__SERVICE_WAIT"] = "300"
 
 from sae_lens import LanguageModelSAERunnerConfig, language_model_sae_runner
 
+# NOTE: Refer to training tutorials for updated parameter configurations. 
+# Tutorial notebook: https://github.com/jbloomAus/SAELens/blob/main/tutorials/training_a_sparse_autoencoder.ipynb
+# PRs to update docs welcome
 cfg = LanguageModelSAERunnerConfig(
-
     # Data Generating Function (Model + Training Distibuion)
-    model_name = "gpt2-small",
-    hook_point = "blocks.2.hook_resid_pre",
-    hook_point_layer = 2,
-    d_in = 768,
-    dataset_path = "Skylion007/openwebtext",
-    is_dataset_tokenized=False,
-
+    model_name="tiny-stories-1L-21M",  # our model (more options here: https://neelnanda-io.github.io/TransformerLens/generated/model_properties_table.html)
+    hook_point="blocks.0.hook_mlp_out",  # A valid hook point (see more details here: https://neelnanda-io.github.io/TransformerLens/generated/demos/Main_Demo.html#Hook-Points)
+    hook_point_layer=0,  # Only one layer in the model.
+    d_in=1024,  # the width of the mlp output.
+    dataset_path="apollo-research/roneneldan-TinyStories-tokenizer-gpt2",  # this is a tokenized language dataset on Huggingface for the Tiny Stories corpus.
+    is_dataset_tokenized=True,
     # SAE Parameters
-    expansion_factor = 64,
-    b_dec_init_method = "geometric_median",
-
+    mse_loss_normalization=None,  # We won't normalize the mse loss,
+    expansion_factor=16,  # the width of the SAE. Larger will result in better stats but slower training.
+    b_dec_init_method="geometric_median",  # The geometric median can be used to initialize the decoder weights.
     # Training Parameters
-    lr = 0.0004,
-    l1_coefficient = 0.00008,
-    lr_scheduler_name="constantwithwarmup",
-    train_batch_size = 4096,
-    context_size = 128,
-    lr_warm_up_steps=5000,
-
+    lr=0.0008,  # lower the better, we'll go fairly high to speed up the tutorial.
+    lr_scheduler_name="constant",  # constant learning rate with warmup. Could be better schedules out there.
+    lr_warm_up_steps=10000,  # this can help avoid too many dead features initially.
+    l1_coefficient=0.001,  # will control how sparse the feature activations are
+    lp_norm=1.0,  # the L1 penalty (and not a Lp for p < 1)
+    train_batch_size=4096,
+    context_size=512,  # will control the lenght of the prompts we feed to the model. Larger is better but slower.
     # Activation Store Parameters
-    n_batches_in_buffer = 128,
-    training_tokens = 1_000_000 * 300,
-    store_batch_size = 32,
-
-    # Dead Neurons and Sparsity
-    use_ghost_grads=True,
-    feature_sampling_window = 1000,
-    dead_feature_window=5000,
-    dead_feature_threshold = 1e-6,
-
+    n_batches_in_buffer=64,  # controls how many activations we store / shuffle.
+    training_tokens=1_000_000
+    * 50,  # 100 million tokens is quite a few, but we want to see good stats. Get a coffee, come back.
+    store_batch_size=16,
+    # Resampling protocol
+    use_ghost_grads=False,
+    feature_sampling_window=1000,  # this controls our reporting of feature sparsity stats
+    dead_feature_window=1000,  # would effect resampling or ghost grads if we were using it.
+    dead_feature_threshold=1e-4,  # would effect resampling or ghost grads if we were using it.
     # WANDB
-    log_to_wandb = True,
-    wandb_project= "gpt2",
-    wandb_entity = None,
-    wandb_log_frequency=100,
-
+    log_to_wandb=True,  # always use wandb unless you are just testing code.
+    wandb_project="sae_lens_tutorial",
+    wandb_log_frequency=10,
     # Misc
-    device = "cuda",
-    seed = 42,
-    n_checkpoints = 10,
-    checkpoint_path = "checkpoints",
-    dtype = torch.float32,
-    )
+    device=device,
+    seed=42,
+    n_checkpoints=0,
+    checkpoint_path="checkpoints",
+    dtype=torch.float32,
+)
 
 sparse_autoencoder = language_model_sae_runner(cfg)