diff --git a/README.md b/README.md
index 6926d69..e6463d5 100644
--- a/README.md
+++ b/README.md
@@ -99,12 +99,11 @@ options:
 
 ### Batch Inference Usage
 
-If you want run a fine tuned model in order to linearize millions of PDFs, you need to use the [birrpipeline.py](https://github.com/allenai/pdelfin/blob/main/pdelfin/birrpipeline.py) script.
+Below are instructions to use mise/birr to conver pdfs:
 
 birrpipeline.py will take as input all of your PDFs (stored in S3), and generate the inputs needed to run those through your fine-tuned model.
 After that, you will use [birr](https://github.com/allenai/mise/tree/main/birr) (part of mise) in order to run those batch inference files efficiently via VLLM.
 
-You should expect somewhere between 1,400 to 1,800 tokens per second per H100 GPU.
 
 ```
 usage: birrpipeline.py [-h] [--add_pdfs ADD_PDFS] [--target_longest_image_dim TARGET_LONGEST_IMAGE_DIM] [--target_anchor_text_len TARGET_ANCHOR_TEXT_LEN] [--workspace_profile WORKSPACE_PROFILE]
diff --git a/pdelfin/beakerpipeline.py b/pdelfin/beakerpipeline.py
index b0ad930..e5a251d 100644
--- a/pdelfin/beakerpipeline.py
+++ b/pdelfin/beakerpipeline.py
@@ -531,6 +531,21 @@ def submit_beaker_job(args):
         b.secret.write(f"{owner}-WEKA_SECRET_ACCESS_KEY", os.environ.get("WEKA_SECRET_ACCESS_KEY", ""), args.beaker_workspace)
         b.secret.write(f"{owner}-AWS_CREDENTIALS_FILE", open(os.path.join(os.path.expanduser('~'), '.aws', 'credentials')).read(), args.beaker_workspace)
 
+    try:
+        b.secret.get(f"OE_DATA_GCS_SA_KEY", args.beaker_workspace)
+        raise SecretNotFound
+    except SecretNotFound:
+        print("Input the olmo-gcs SA key if you would like to load weights from gcs (end with a double newline):")
+        lines = []
+        prev_empty = False
+        for line in iter(input, None):
+            if not line and prev_empty:
+                break
+            prev_empty = not line
+            lines.append(line)
+        gcs_sa_key = "\n".join(lines[:-1]).strip()  # Remove the last empty line
+        if gcs_sa_key:
+            b.secret.write(f"OE_DATA_GCS_SA_KEY", gcs_sa_key, args.beaker_workspace)
 
     # Create the experiment spec
     experiment_spec = ExperimentSpec(
@@ -554,6 +569,7 @@ def submit_beaker_job(args):
                     EnvVar(name="WEKA_ACCESS_KEY_ID", secret=f"{owner}-WEKA_ACCESS_KEY_ID"),
                     EnvVar(name="WEKA_SECRET_ACCESS_KEY", secret=f"{owner}-WEKA_SECRET_ACCESS_KEY"),
                     EnvVar(name="AWS_CREDENTIALS_FILE", secret=f"{owner}-AWS_CREDENTIALS_FILE"),
+                    EnvVar(name="GOOGLE_APPLICATION_CREDENTIALS", secret=f"OE_DATA_GCS_SA_KEY"),
                 ],
                 resources=TaskResources(gpu_count=1),
                 constraints=Constraints(cluster=args.beaker_cluster if isinstance(args.beaker_cluster, list) else [args.beaker_cluster]),
diff --git a/pdelfin/s3_utils.py b/pdelfin/s3_utils.py
index d1c71ea..fb66666 100644
--- a/pdelfin/s3_utils.py
+++ b/pdelfin/s3_utils.py
@@ -180,7 +180,7 @@ def download_directory(model_choices: List[str], local_dir: str):
     weka_choices = [path for path in model_choices if path.startswith("weka://")]
 
     # This is so hacky, but if you are on beaker/pluto, don't use weka
-    if os.environ.get("BEAKER_NODE_HOSTNAME", "").lower().startswith("pluto"):
+    if os.environ.get("BEAKER_NODE_HOSTNAME", "").lower().startswith("pluto") or os.environ.get("BEAKER_NODE_HOSTNAME", "").lower().startswith("augusta"):
         weka_choices = []
 
     other_choices = [path for path in model_choices if not path.startswith("weka://")]
diff --git a/pdelfin/version.py b/pdelfin/version.py
index d1e204d..8ab463f 100644
--- a/pdelfin/version.py
+++ b/pdelfin/version.py
@@ -2,7 +2,7 @@
 _MINOR = "1"
 # On main and in a nightly release the patch should be one ahead of the last
 # released build.
-_PATCH = "26"
+_PATCH = "27"
 # This is mainly for nightly builds which have the suffix ".dev$DATE". See
 # https://semver.org/#is-v123-a-semantic-version for the semantics.
 _SUFFIX = ""