diff --git a/modeling/inference_models/generic_hf_torch/class.py b/modeling/inference_models/generic_hf_torch/class.py
index 0bb954e3..25d49214 100644
--- a/modeling/inference_models/generic_hf_torch/class.py
+++ b/modeling/inference_models/generic_hf_torch/class.py
@@ -36,13 +36,14 @@ class model_backend(HFTorchInferenceModel):
                 else:
                     temp = {}
                 requested_parameters.append({
-                                            "uitype": "toggle",
-                                            "unit": "bool",
-                                            "label": "Use 4-bit",
-                                            "id": "use_4_bit",
-                                            "default": temp['use_4_bit'] if 'use_4_bit' in temp else False,
-                                            "tooltip": "Whether or not to use BnB's 4-bit mode",
+                                            "uitype": "dropdown",
+                                            "unit": "text",
+                                            "label": "Quantization",
+                                            "id": "quantization",
+                                            "default": temp['quantization'] if 'quantization' in temp else 'none',
+                                            "tooltip": "Whether or not to use BnB's 4-bit or 8-bit mode",
                                             "menu_path": "Layers",
+                                            "children": [{'text': 'None', 'value':'none'},{'text': '4-bit', 'value': '4bit'}, {'text': '8-bit', 'value': '8bit'}],
                                             "extra_classes": "",
                                             "refresh_model_inputs": False
                                         })
@@ -52,7 +53,7 @@ class model_backend(HFTorchInferenceModel):
  
     def set_input_parameters(self, parameters):
         super().set_input_parameters(parameters)
-        self.use_4_bit = parameters['use_4_bit'] if 'use_4_bit' in parameters else False
+        self.quantization = parameters['quantization'] if 'quantization' in parameters else False
 
     def _load(self, save_model: bool, initial_load: bool) -> None:
         utils.koboldai_vars.allowsp = True
@@ -82,7 +83,15 @@ class model_backend(HFTorchInferenceModel):
             "low_cpu_mem_usage": True,
         }
         
-        if self.use_4_bit or utils.koboldai_vars.colab_arg:
+        if self.quantization == "8bit":
+            tf_kwargs.update({
+                "quantization_config":BitsAndBytesConfig(
+                    load_in_8bit=True,
+                    llm_int8_enable_fp32_cpu_offload=True
+                ),
+            })
+
+        if self.quantization == "4bit" or utils.koboldai_vars.colab_arg:
             tf_kwargs.update({
                 "quantization_config":BitsAndBytesConfig(
                     load_in_4bit=True,
@@ -297,7 +306,7 @@ class model_backend(HFTorchInferenceModel):
                     "disk_layers": self.disk_layers
                     if "disk_layers" in vars(self)
                     else 0,
-                    "use_4_bit": self.use_4_bit,
+                    "quantization": self.quantization,
                 },
                 f,
                 indent="",
diff --git a/static/koboldai.js b/static/koboldai.js
index 94ac6ce4..8b70dd6a 100644
--- a/static/koboldai.js
+++ b/static/koboldai.js
@@ -2011,7 +2011,7 @@ function load_model() {
 	data = {}
 	if (settings_area) {
 		for (const element of settings_area.querySelectorAll(".model_settings_input:not(.hidden)")) {
-			var element_data = element.value;
+			var element_data = element.getAttribute("data_type") === "bool" ? element.checked : element.value;
 			if ((element.tagName == "SELECT") && (element.multiple)) {
 				element_data = [];
 				for (var i=0, iLen=element.options.length; i<iLen; i++) {
@@ -2024,8 +2024,6 @@ function load_model() {
 					element_data = parseInt(element_data);
 				} else if (element.getAttribute("data_type") == "float") {
 					element_data = parseFloat(element_data);
-				} else if (element.getAttribute("data_type") == "bool") {
-					element_data = (element_data == 'on');
 				}
 			}
 			data[element.id.split("|")[1].replace("_value", "")] = element_data;