diff --git a/README.md b/README.md
index 3f02770..e6f5896 100755
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ This is a simple ResNet based image classifier for """specific images""", using
 
 ## Inferencing
 
-To be implemented.
+Simply invoke the inferencer with the following command: `python3 -m captcha "./data/path-to-your-image.png" yaml="./data/config.yaml" --temp=1.0`
 
 ## Caveats
 
diff --git a/captcha/train.py b/captcha/train.py
index e9ce99d..1a02779 100755
--- a/captcha/train.py
+++ b/captcha/train.py
@@ -57,7 +57,7 @@ def run_eval(engines, eval_name, dl):
 		batch: dict = to_device(batch, cfg.device)
 
 		# if we're training both models, provide output for both
-		res = model( image=batch['image'], text=batch['text'], temperature=cfg.evaluation.temperature )
+		res = model( image=batch['image'], text=batch['text'], sampling_temperature=cfg.evaluation.temperature )
 
 		for path, ref, hyp in zip(batch["path"], batch["text"], res):
 			hyp = hyp.replace('<s>', "").replace("</s>", "")
@@ -68,7 +68,7 @@ def run_eval(engines, eval_name, dl):
 			image.save(hyp_path)
 
 		losses = engine.gather_attribute("loss")
-		loss = torch.stack([*losses.values()]).sum()
+		loss = torch.stack([*losses.values()]).sum().item()
 
 		stats['loss'].append(loss)