diff --git a/examples/openwebtext/README.md b/examples/openwebtext/README.md
index 99d0bf7..f10ece0 100644
--- a/examples/openwebtext/README.md
+++ b/examples/openwebtext/README.md
@@ -14,7 +14,7 @@ We will use the pre-trained Meta-Llama-3-8B model [from HuggingFace](https://hug
 To compute factors using the `ekfac` factorization strategy, run the following command which uses 4 A100 (80GB) GPUs:
 
 ```bash
-torchrun --standalone --nnodes=1 --nproc-per-node=4 fit_factors.py --factor_batch_size 4
+torchrun --standalone --nnodes=1 --nproc-per-node=4 fit_factors.py --factors_name jul_11_2024 --factor_batch_size 4
 ```
 
 ## Computing Influence Scores
@@ -25,5 +25,5 @@ I saved some prompt and completition pair to the directory `data/data.json`.
 To compute influence scores on the generated prompt and compleition pair, run the following command:
 
 ```bash
-torchrun --standalone --nnodes=1 --nproc-per-node=4 compute_scores.py --train_batch_size 8
+torchrun --standalone --nnodes=1 --nproc-per-node=4 compute_scores.py --train_batch_size 8 --query_gradient_rank 32
 ```
\ No newline at end of file
diff --git a/examples/openwebtext/fit_factors.py b/examples/openwebtext/fit_factors.py
index c96536f..740f349 100644
--- a/examples/openwebtext/fit_factors.py
+++ b/examples/openwebtext/fit_factors.py
@@ -26,6 +26,12 @@
 def parse_args():
     parser = argparse.ArgumentParser(description="Influence factor computation on Openwebtext dataset.")
 
+    parser.add_argument(
+        "--factors_name",
+        type=str,
+        default="july_11",
+        help="Strategy to compute influence factors.",
+    )
     parser.add_argument(
         "--factor_strategy",
         type=str,
@@ -77,7 +83,7 @@ def main():
     dataloader_kwargs = DataLoaderKwargs(num_workers=4, collate_fn=default_data_collator, pin_memory=True)
     analyzer.set_dataloader_kwargs(dataloader_kwargs)
 
-    factors_name = args.factor_strategy
+    factors_name = args.factors_name
     factor_args = extreme_reduce_memory_factor_arguments(
         strategy=args.factor_strategy, module_partitions=1, dtype=torch.bfloat16
     )