diff --git a/examples/run_svi.py b/examples/run_svi.py
index a3a905e..a55af8f 100644
--- a/examples/run_svi.py
+++ b/examples/run_svi.py
@@ -10,25 +10,40 @@
 parser = argparse.ArgumentParser(description="Train DeepSequence with SVI.")
 parser.add_argument("--dataset", type=str, default="BLAT_ECOLX",
                     help="Dataset name for fitting model.")
+parser.add_argument("--neff-override", type=float, default=None,
+                    help="Override the model Neff.")
+parser.add_argument("--theta-override", type=float, default=None,
+                    help="Override the model theta.")
+parser.add_argument("--n-latent-override", type=int, default=None,
+                    help="Override the model n_latent.")
+parser.add_argument("--weights_dir", type=str, default="", help="Location of precomputed weights, if possible")
+parser.add_argument("--alignments_dir", type=str, help="Location of alignments")
+parser.add_argument("--seed", type=int, help="Random seed override (for model ensembling).")
 args = parser.parse_args()
 
+args.dataset = args.dataset.split(".a2m")[0]
+
 data_params = {
-    "dataset"           :   args.dataset,
+    "dataset"       :   args.dataset,
+    "weights_dir"	:   args.weights_dir,
     }
 
+if args.seed is not None:
+    print("Using seed: {}".format(args.seed))
+
 model_params = {
     "bs"                :   100,
     "encode_dim_zero"   :   1500,
     "encode_dim_one"    :   1500,
     "decode_dim_zero"   :   100,
-    "decode_dim_one"    :   500,
+    "decode_dim_one"    :   2000,  # 500 in the repo
     "n_latent"          :   30,
     "logit_p"           :   0.001,
     "sparsity"          :   "logit",
     "final_decode_nonlin":  "sigmoid",
     "final_pwm_scale"   :   True,
     "n_pat"             :   4,
-    "r_seed"            :   12345,
+    "r_seed"            :   args.seed if args.seed is not None else 12345,
     "conv_pat"          :   True,
     "d_c_size"          :   40
     }
@@ -37,13 +52,24 @@
     "num_updates"       :   300000,
     "save_progress"     :   True,
     "verbose"           :   True,
-    "save_parameters"   :   False,
+    "save_parameters"   :   50000,
     }
 
-if __name__ == "__main__":
+if args.n_latent_override:
+    model_params['n_latent'] = args.n_latent_override
 
+if __name__ == "__main__":
+    start_time = time.time()
     data_helper = helper.DataHelper(dataset=data_params["dataset"],
-                                    calc_weights=True)
+                                    working_dir='.',
+                                    calc_weights=False,  # Use precomputed weights
+                                    theta=args.theta_override,
+				                    weights_dir=data_params["weights_dir"],
+                                    alignments_dir=args.alignments_dir,
+    )
+    print("Data loaded.")
+    if args.neff_override:
+        data_helper.Neff = args.neff_override
 
     vae_model   = model.VariationalAutoencoder(data_helper,
         batch_size                     =   model_params["bs"],
@@ -63,10 +89,17 @@
         n_patterns                     =   model_params["n_pat"],
         random_seed                    =   model_params["r_seed"],
         )
+    print("Model loaded")
 
     job_string = helper.gen_job_string(data_params, model_params)
+    if args.neff_override:
+        job_string += "_neff-" + str(args.neff_override)
+    if args.seed is not None:
+        job_string += "_seed-" + str(args.seed)
+
+    print ("job string: ", job_string)
 
-    print (job_string)
+    print("Starting training")
 
     train.train(data_helper, vae_model,
         num_updates             =   train_params["num_updates"],
@@ -74,5 +107,8 @@
         save_parameters         =   train_params["save_parameters"],
         verbose                 =   train_params["verbose"],
         job_string              =   job_string)
+    print("Training complete")
 
     vae_model.save_parameters(file_prefix=job_string)
+
+    print("Done in " + str(time.time() - start_time) + " seconds")