diff --git a/modeling/inference_models/gptq_hf_torch/class.py b/modeling/inference_models/gptq_hf_torch/class.py
index b48f1d56..b6c9b944 100644
--- a/modeling/inference_models/gptq_hf_torch/class.py
+++ b/modeling/inference_models/gptq_hf_torch/class.py
@@ -228,12 +228,14 @@ class model_backend(HFTorchInferenceModel):
                     logger.warning(f"Gave up on lazy loading due to {e}")
                     self.lazy_load = False
 
-        if self.get_local_model_path():
-            # Model is stored locally, load it.
-            self.model = self._get_model(self.get_local_model_path())
-            self.tokenizer = self._get_tokenizer(self.get_local_model_path())
-        else:
-            raise NotImplementedError("GPTQ Model downloading not implemented")
+        if not self.get_local_model_path():
+            print(self.get_local_model_path())
+            from huggingface_hub import snapshot_download
+            target_dir = "models/" + self.model_name.replace("/", "_")
+            snapshot_download(self.model_name, local_dir=target_dir, local_dir_use_symlinks=False, cache_dir="cache/")
+        
+        self.model = self._get_model(self.get_local_model_path())
+        self.tokenizer = self._get_tokenizer(self.get_local_model_path())
 
         if (
             utils.koboldai_vars.badwordsids is koboldai_settings.badwordsids_default
@@ -350,23 +352,20 @@ class model_backend(HFTorchInferenceModel):
             dematerialized_modules=False,
         ):
             if self.implementation == "occam":
-                try:
-                    if model_type == "gptj":
-                        model = load_quant_offload_device_map(gptj_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias)
-                    elif model_type == "gpt_neox":
-                        model = load_quant_offload_device_map(gptneox_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias)
-                    elif model_type == "llama":
-                        model = load_quant_offload_device_map(llama_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias)
-                    elif model_type == "opt":
-                        model = load_quant_offload_device_map(opt_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias)
-                    elif model_tseype == "mpt":
-                        model = load_quant_offload_device_map(mpt_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias)
-                    elif model_type == "gpt_bigcode":
-                        model = load_quant_offload_device_map(bigcode_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias).half()
-                    else:
-                        raise RuntimeError("Model not supported by Occam's GPTQ")
-                except:
-                    self.implementation = "AutoGPTQ"
+                if model_type == "gptj":
+                    model = load_quant_offload_device_map(gptj_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias)
+                elif model_type == "gpt_neox":
+                    model = load_quant_offload_device_map(gptneox_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias)
+                elif model_type == "llama":
+                    model = load_quant_offload_device_map(llama_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias)
+                elif model_type == "opt":
+                    model = load_quant_offload_device_map(opt_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias)
+                elif model_tseype == "mpt":
+                    model = load_quant_offload_device_map(mpt_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias)
+                elif model_type == "gpt_bigcode":
+                    model = load_quant_offload_device_map(bigcode_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias).half()
+                else:
+                    raise RuntimeError("Model not supported by Occam's GPTQ")
             if self.implementation == "AutoGPTQ":
                 try:
                     import auto_gptq