diff --git a/scripts/finetune.py b/scripts/finetune.py
index ba3a59a6a..4c24a3c4f 100644
--- a/scripts/finetune.py
+++ b/scripts/finetune.py
@@ -79,31 +79,31 @@ def do_inference(cfg, model, tokenizer):
 
     from axolotl.prompters import ReflectAlpacaPrompter
 
-    instruction = str(input("Give me an instruction: "))
-    instruction = (
-        instruction if not instruction else "Tell me a joke about dromedaries."
-    )
-    prompt = ReflectAlpacaPrompter().build_prompt(instruction=instruction)
-    batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+    while True:
+        instruction = str(input("Give me an instruction: "))
+        if not instruction:
+            return
+        prompt = ReflectAlpacaPrompter().build_prompt(instruction=instruction)
+        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
-    model.eval()
-    with torch.no_grad():
-        # gc = GenerationConfig()  # TODO swap out and use this
-        generated = model.generate(
-            inputs=batch["input_ids"].to("cuda"),
-            do_sample=True,
-            use_cache=True,
-            repetition_penalty=1.1,
-            max_new_tokens=100,
-            temperature=0.9,
-            top_p=0.95,
-            top_k=40,
-            return_dict_in_generate=True,
-            output_attentions=False,
-            output_hidden_states=False,
-            output_scores=False,
-        )
-    print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
+        model.eval()
+        with torch.no_grad():
+            # gc = GenerationConfig()  # TODO swap out and use this
+            generated = model.generate(
+                inputs=batch["input_ids"].to("cuda"),
+                do_sample=True,
+                use_cache=True,
+                repetition_penalty=1.1,
+                max_new_tokens=100,
+                temperature=0.9,
+                top_p=0.95,
+                top_k=40,
+                return_dict_in_generate=True,
+                output_attentions=False,
+                output_hidden_states=False,
+                output_scores=False,
+            )
+        print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
 
 
 def choose_config(path: Path):
diff --git a/src/axolotl/utils/models.py b/src/axolotl/utils/models.py
index d05e62d29..d66831861 100644
--- a/src/axolotl/utils/models.py
+++ b/src/axolotl/utils/models.py
@@ -66,22 +66,25 @@ def load_model(
             from alpaca_lora_4bit.autograd_4bit import load_llama_model_4bit_low_ram
             from huggingface_hub import snapshot_download
 
-            snapshot_download_kwargs = {}
-            if cfg.base_model_ignore_patterns:
-                snapshot_download_kwargs["ignore_patterns"] = cfg.base_model_ignore_patterns
-            cache_model_path = Path(snapshot_download(base_model, ** snapshot_download_kwargs))
-            files = (
-                list(cache_model_path.glob("*.pt"))
-                + list(cache_model_path.glob("*.safetensors"))
-                + list(cache_model_path.glob("*.bin"))
-            )
-            if len(files) > 0:
-                model_path = str(files[0])
-            else:
-                logging.warning(
-                    "unable to find a cached model file, this will likely fail..."
+            try:
+                snapshot_download_kwargs = {}
+                if cfg.base_model_ignore_patterns:
+                    snapshot_download_kwargs["ignore_patterns"] = cfg.base_model_ignore_patterns
+                cache_model_path = Path(snapshot_download(base_model, ** snapshot_download_kwargs))
+                files = (
+                    list(cache_model_path.glob("*.pt"))
+                    + list(cache_model_path.glob("*.safetensors"))
+                    + list(cache_model_path.glob("*.bin"))
                 )
-                model_path = str(cache_model_path)
+                if len(files) > 0:
+                    model_path = str(files[0])
+                else:
+                    logging.warning(
+                        "unable to find a cached model file, this will likely fail..."
+                    )
+                    model_path = str(cache_model_path)
+            except:
+                model_path = cfg.base_model
             model, tokenizer = load_llama_model_4bit_low_ram(
                 base_model_config if base_model_config else base_model,
                 model_path,