feat: support multiple quantization types

This commit adds the ability to select and run multiple quantization types simultaneously. It includes:
- Replacing the quantization type dropdown with a multi-select list
- Updating preset saving and loading to handle multiple quantization types
- Modifying the quantize_model function to process all selected types
- fix formatting issue with previous commit
- use error and in progress messages from localizations in QuantizationThread

docs/AutoGGUF.py

@@ -125,10 +125,25 @@ def load_models(self):
 
     def quantize_model(self):
         """
-        Start the model quantization process.
+        Initiates the quantization process for the selected model with the chosen quantization types.
 
-        This method collects all the quantization settings, constructs the
+        This function performs the following steps:
-        quantization command, and starts a new thread to run the quantization process.
+        1. Validates the input parameters and selected model.
+        2. Retrieves the backend path and selected quantization types.
+        3. For each selected quantization type:
+            a. Constructs the output file name based on the quantization options.
+            b. Builds the quantization command with all selected options.
+            c. Creates a new thread for the quantization process.
+            d. Sets up a task item in the GUI to display progress and status.
+            e. Connects the thread signals to update the GUI and handle errors.
+            f. Starts the quantization thread.
+
+        The function handles various exceptions and displays error messages if any step fails.
+
+        Raises:
+            ValueError: If required inputs are missing or invalid.
+            FileNotFoundError: If the input model file doesn't exist.
+            Exception: For any other unexpected errors during the process.
         """
 
     def update_model_info(self, model_info):

src/AutoGGUF.py

@@ -144,9 +144,11 @@ def __init__(self):
         quant_options_widget = QWidget()
         quant_options_layout = QFormLayout()
 
-        self.quant_type = QComboBox()
+        self.quant_type = QListWidget()
-        self.quant_type.addItems(
+        self.quant_type.setMinimumHeight(100)
-            [
+        self.quant_type.setMinimumWidth(150)
+        self.quant_type.setSelectionMode(QListWidget.SelectionMode.MultiSelection)
+        quant_types = [
             "IQ2_XXS",
             "IQ2_XS",
             "IQ2_S",
@@ -185,7 +187,7 @@ def __init__(self):
             "F32",
             "COPY",
         ]
-        )
+        self.quant_type.addItems(quant_types)
         quant_options_layout.addRow(
             self.create_label(QUANTIZATION_TYPE, SELECT_QUANTIZATION_TYPE),
             self.quant_type,
@@ -565,6 +567,9 @@ def __init__(self):
         # Initialize threads
         self.quant_threads = []
 
+        # Load models
+        self.load_models()
+
         self.logger.info(AUTOGGUF_INITIALIZATION_COMPLETE)
 
     def refresh_backends(self):
@@ -598,7 +603,7 @@ def update_base_model_visibility(self, index):
     def save_preset(self):
         self.logger.info(SAVING_PRESET)
         preset = {
-            "quant_type": self.quant_type.currentText(),
+            "quant_types": [item.text() for item in self.quant_type.selectedItems()],
             "allow_requantize": self.allow_requantize.isChecked(),
             "leave_output_tensor": self.leave_output_tensor.isChecked(),
             "pure": self.pure.isChecked(),
@@ -633,7 +638,11 @@ def load_preset(self):
                 with open(file_name, "r") as f:
                     preset = json.load(f)
 
-                self.quant_type.setCurrentText(preset.get("quant_type", ""))
+                self.quant_type.clearSelection()
+                for quant_type in preset.get("quant_types", []):
+                    items = self.quant_type.findItems(quant_type, Qt.MatchExactly)
+                    if items:
+                        items[0].setSelected(True)
                 self.allow_requantize.setChecked(preset.get("allow_requantize", False))
                 self.leave_output_tensor.setChecked(
                     preset.get("leave_output_tensor", False)
@@ -1364,10 +1373,20 @@ def quantize_model(self):
             backend_path = self.backend_combo.currentData()
             if not backend_path:
                 raise ValueError(NO_BACKEND_SELECTED)
-            quant_type = self.quant_type.currentText()
+
+            selected_quant_types = [
+                item.text() for item in self.quant_type.selectedItems()
+            ]
+            if not selected_quant_types:
+                raise ValueError(NO_QUANTIZATION_TYPE_SELECTED)
 
             input_path = os.path.join(self.models_input.text(), model_file)
+            if not os.path.exists(input_path):
+                raise FileNotFoundError(INPUT_FILE_NOT_EXIST.format(input_path))
 
+            tasks = []  # List to keep track of all tasks
+
+            for quant_type in selected_quant_types:
                 # Start building the output name
                 output_name_parts = [
                     os.path.splitext(model_name)[0],
@@ -1382,7 +1401,9 @@ def quantize_model(self):
                 ):
                     output_tensor_part = "o"
                     if self.use_output_tensor_type.isChecked():
-                    output_tensor_part += "." + self.output_tensor_type.currentText()
+                        output_tensor_part += (
+                            "." + self.output_tensor_type.currentText()
+                        )
                     output_name_parts.append(output_tensor_part)
 
                 # Check for embedding tensor options
@@ -1399,15 +1420,14 @@ def quantize_model(self):
                     output_name_parts.append("rq")
 
                 # Check for KV override
-            if any(entry.get_override_string() for entry in self.kv_override_entries):
+                if any(
+                    entry.get_override_string() for entry in self.kv_override_entries
+                ):
                     output_name_parts.append("kv")
 
                 # Join all parts with underscores and add .gguf extension
                 output_name = "_".join(output_name_parts) + ".gguf"
-
                 output_path = os.path.join(self.output_input.text(), output_name)
-            if not os.path.exists(input_path):
-                raise FileNotFoundError(INPUT_FILE_NOT_EXIST.format(input_path))
 
                 command = [os.path.join(backend_path, "llama-quantize")]
 
@@ -1429,7 +1449,10 @@ def quantize_model(self):
                     )
                 if self.use_token_embedding_type.isChecked():
                     command.extend(
-                    ["--token-embedding-type", self.token_embedding_type.currentText()]
+                        [
+                            "--token-embedding-type",
+                            self.token_embedding_type.currentText(),
+                        ]
                     )
                 if self.keep_split.isChecked():
                     command.append("--keep-split")
@@ -1472,18 +1495,32 @@ def quantize_model(self):
                 self.task_list.addItem(list_item)
                 self.task_list.setItemWidget(list_item, task_item)
 
+                tasks.append(
+                    (thread, task_item)
+                )  # Add the thread and task_item to our list
+
                 # Connect the output signal to the new progress parsing function
                 thread.output_signal.connect(
-                lambda line: self.parse_progress(line, task_item)
+                    lambda line, ti=task_item: self.parse_progress(line, ti)
                 )
                 thread.status_signal.connect(task_item.update_status)
-            thread.finished_signal.connect(lambda: self.task_finished(thread))
+                thread.finished_signal.connect(
-            thread.error_signal.connect(lambda err: self.handle_error(err, task_item))
+                    lambda t=thread, ti=task_item: self.task_finished(t, ti)
+                )
+                thread.error_signal.connect(
+                    lambda err, ti=task_item: self.handle_error(err, ti)
+                )
                 thread.model_info_signal.connect(self.update_model_info)
+
+            # Start all threads after setting them up
+            for thread, _ in tasks:
                 thread.start()
                 self.logger.info(QUANTIZATION_TASK_STARTED.format(model_name))
+
         except ValueError as e:
             self.show_error(str(e))
+        except FileNotFoundError as e:
+            self.show_error(str(e))
         except Exception as e:
             self.show_error(ERROR_STARTING_QUANTIZATION.format(str(e)))
 
@@ -1501,10 +1538,11 @@ def parse_progress(self, line, task_item):
             progress = int((current / total) * 100)
             task_item.update_progress(progress)
 
-    def task_finished(self, thread):
+    def task_finished(self, thread, task_item):
         self.logger.info(TASK_FINISHED.format(thread.log_file))
         if thread in self.quant_threads:
             self.quant_threads.remove(thread)
+        task_item.update_status(COMPLETED)
 
     def show_task_details(self, item):
         self.logger.debug(SHOWING_TASK_DETAILS_FOR.format(item.text()))
@@ -1630,11 +1668,10 @@ def show_error(self, message):
         self.logger.error(ERROR_MESSAGE.format(message))
         QMessageBox.critical(self, ERROR, message)
 
-    def handle_error(self, error_message, task_item, task_exists=True):
+    def handle_error(self, error_message, task_item):
         self.logger.error(TASK_ERROR.format(error_message))
         self.show_error(error_message)
-        if task_exists:
+        task_item.update_status(ERROR)
-            task_item.set_error()
 
     def closeEvent(self, event: QCloseEvent):
         self.logger.info(APPLICATION_CLOSING)

src/QuantizationThread.py

@@ -14,6 +14,7 @@
 import traceback
 from datetime import datetime
 from imports_and_globals import open_file_safe
+from localizations import *
 
 
 class QuantizationThread(QThread):
@@ -49,13 +50,13 @@ def run(self):
                     self.output_signal.emit(line)
                     log.write(line + "\n")
                     log.flush()
-                    self.status_signal.emit("In Progress")
+                    self.status_signal.emit(IN_PROGRESS)
                     self.parse_model_info(line)
 
             # Wait for process to complete
             self.process.wait()
             if self.process.returncode == 0:
-                self.status_signal.emit("Completed")
+                self.status_signal.emit(COMPLETED)
                 self.model_info_signal.emit(self.model_info)
             else:
                 self.error_signal.emit(

src/localizations.py

@@ -230,6 +230,7 @@ def __init__(self):
         self.IMATRIX_GENERATION_COMMAND = ""
         self.LORA_CONVERSION_COMMAND = ""
         self.LORA_EXPORT_COMMAND = ""
+        self.NO_QUANTIZATION_TYPE_SELECTED = ""
 
 
 class _English(_Localization):
@@ -502,6 +503,7 @@ def __init__(self):
         self.IMATRIX_GENERATION_COMMAND = "IMatrix generation command"
         self.LORA_CONVERSION_COMMAND = "LoRA conversion command"
         self.LORA_EXPORT_COMMAND = "LoRA export command"
+        self.NO_QUANTIZATION_TYPE_SELECTED = "No quantization type selected. Please select at least one quantization type."
 
 
 class _French:
@@ -857,7 +859,9 @@ def __init__(self):
         self.NO_MODEL_SELECTED = "未选择模型"
         self.REFRESH_RELEASES = "刷新发布版本"
         self.NO_SUITABLE_CUDA_BACKENDS = "未找到合适的CUDA后端"
-        self.LLAMACPP_DOWNLOADED_EXTRACTED = "llama.cpp二进制文件已下载并提取到{0}\nCUDA文件已提取到{1}"
+        self.LLAMACPP_DOWNLOADED_EXTRACTED = (
+            "llama.cpp二进制文件已下载并提取到{0}\nCUDA文件已提取到{1}"
+        )
         self.CUDA_FILES_EXTRACTED = "CUDA文件已提取到"
         self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = "未找到适合提取的CUDA后端"
         self.ERROR_FETCHING_RELEASES = "获取发布版本时出错：{0}"
@@ -888,7 +892,9 @@ def __init__(self):
             "llama.cpp二进制文件已下载并提取到{0}\nCUDA文件已提取到{1}"
         )
         self.NO_SUITABLE_CUDA_BACKEND_FOUND = "未找到适合提取的CUDA后端"
-        self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = "llama.cpp二进制文件已下载并提取到{0}"
+        self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = (
+            "llama.cpp二进制文件已下载并提取到{0}"
+        )
         self.REFRESHING_LLAMACPP_RELEASES = "刷新llama.cpp发布版本"
         self.UPDATING_ASSET_LIST = "更新资源列表"
         self.UPDATING_CUDA_OPTIONS = "更新CUDA选项"
@@ -952,7 +958,9 @@ def __init__(self):
         self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "对output.weight张量使用此类型"
         self.TOKEN_EMBEDDING_TYPE = "令牌嵌入类型："
         self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = "对令牌嵌入张量使用此类型"
-        self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = "将在与输入相同的分片中生成量化模型"
+        self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = (
+            "将在与输入相同的分片中生成量化模型"
+        )
         self.OVERRIDE_MODEL_METADATA = "覆盖模型元数据"
         self.INPUT_DATA_FILE_FOR_IMATRIX = "IMatrix生成的输入数据文件"
         self.MODEL_TO_BE_QUANTIZED = "要量化的模型"
@@ -1400,7 +1408,9 @@ def __init__(self):
         self.NO_MODEL_SELECTED = "कोई मॉडल चयनित नहीं"
         self.REFRESH_RELEASES = "रिलीज़ रीफ्रेश करें"
         self.NO_SUITABLE_CUDA_BACKENDS = "कोई उपयुक्त CUDA बैकएंड नहीं मिला"
-        self.LLAMACPP_DOWNLOADED_EXTRACTED = "llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं"
+        self.LLAMACPP_DOWNLOADED_EXTRACTED = (
+            "llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं"
+        )
         self.CUDA_FILES_EXTRACTED = "CUDA फ़ाइलें निकाली गईं"
         self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = (
             "निष्कर्षण के लिए कोई उपयुक्त CUDA बैकएंड नहीं मिला"
@@ -1429,7 +1439,9 @@ def __init__(self):
         self.RESTARTING_TASK = "कार्य पुनः आरंभ हो रहा है: {0}"
         self.IN_PROGRESS = "प्रगति में"
         self.DOWNLOAD_FINISHED_EXTRACTED_TO = "डाउनलोड समाप्त। निकाला गया: {0}"
-        self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं"
+        self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = (
+            "llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं"
+        )
         self.NO_SUITABLE_CUDA_BACKEND_FOUND = (
             "निष्कर्षण के लिए कोई उपयुक्त CUDA बैकएंड नहीं मिला"
         )
@@ -1451,25 +1463,17 @@ def __init__(self):
         self.DELETING_TASK = "कार्य हटाया जा रहा है: {0}"
         self.LOADING_MODELS = "मॉडल लोड हो रहे हैं"
         self.LOADED_MODELS = "{0} मॉडल लोड किए गए"
-        self.BROWSING_FOR_MODELS_DIRECTORY = (
+        self.BROWSING_FOR_MODELS_DIRECTORY = "मॉडल निर्देशिका के लिए ब्राउज़ किया जा रहा है"
-            "मॉडल निर्देशिका के लिए ब्राउज़ किया जा रहा है"
-        )
         self.SELECT_MODELS_DIRECTORY = "मॉडल निर्देशिका चुनें"
-        self.BROWSING_FOR_OUTPUT_DIRECTORY = (
+        self.BROWSING_FOR_OUTPUT_DIRECTORY = "आउटपुट निर्देशिका के लिए ब्राउज़ किया जा रहा है"
-            "आउटपुट निर्देशिका के लिए ब्राउज़ किया जा रहा है"
-        )
         self.SELECT_OUTPUT_DIRECTORY = "आउटपुट निर्देशिका चुनें"
-        self.BROWSING_FOR_LOGS_DIRECTORY = (
+        self.BROWSING_FOR_LOGS_DIRECTORY = "लॉग निर्देशिका के लिए ब्राउज़ किया जा रहा है"
-            "लॉग निर्देशिका के लिए ब्राउज़ किया जा रहा है"
-        )
         self.SELECT_LOGS_DIRECTORY = "लॉग निर्देशिका चुनें"
         self.BROWSING_FOR_IMATRIX_FILE = "IMatrix फ़ाइल के लिए ब्राउज़ किया जा रहा है"
         self.SELECT_IMATRIX_FILE = "IMatrix फ़ाइल चुनें"
         self.RAM_USAGE_FORMAT = "{0:.1f}% ({1} MB / {2} MB)"
         self.CPU_USAGE_FORMAT = "CPU उपयोग: {0:.1f}%"
-        self.VALIDATING_QUANTIZATION_INPUTS = (
+        self.VALIDATING_QUANTIZATION_INPUTS = "क्वांटाइजेशन इनपुट सत्यापित किए जा रहे हैं"
-            "क्वांटाइजेशन इनपुट सत्यापित किए जा रहे हैं"
-        )
         self.MODELS_PATH_REQUIRED = "मॉडल पथ आवश्यक है"
         self.OUTPUT_PATH_REQUIRED = "आउटपुट पथ आवश्यक है"
         self.LOGS_PATH_REQUIRED = "लॉग पथ आवश्यक है"
@@ -1496,9 +1500,7 @@ def __init__(self):
         self.STARTING_IMATRIX_GENERATION = "IMatrix उत्पादन शुरू हो रहा है"
         self.BACKEND_PATH_NOT_EXIST = "बैकएंड पथ मौजूद नहीं है: {0}"
         self.GENERATING_IMATRIX = "IMatrix उत्पन्न किया जा रहा है"
-        self.ERROR_STARTING_IMATRIX_GENERATION = (
+        self.ERROR_STARTING_IMATRIX_GENERATION = "IMatrix उत्पादन शुरू करने में त्रुटि: {0}"
-            "IMatrix उत्पादन शुरू करने में त्रुटि: {0}"
-        )
         self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix उत्पादन कार्य शुरू हुआ"
         self.ERROR_MESSAGE = "त्रुटि: {0}"
         self.TASK_ERROR = "कार्य त्रुटि: {0}"
@@ -1508,14 +1510,14 @@ def __init__(self):
         self.ALLOWS_REQUANTIZING = (
             "पहले से क्वांटाइज़ किए गए टेंसर को पुनः क्वांटाइज़ करने की अनुमति देता है"
         )
-        self.LEAVE_OUTPUT_WEIGHT = (
+        self.LEAVE_OUTPUT_WEIGHT = "output.weight को अक्वांटाइज़ (या पुनः क्वांटाइज़) छोड़ देगा"
-            "output.weight को अक्वांटाइज़ (या पुनः क्वांटाइज़) छोड़ देगा"
+        self.DISABLE_K_QUANT_MIXTURES = (
+            "k-quant मिश्रण को अक्षम करें और सभी टेंसर को एक ही प्रकार में क्वांटाइज़ करें"
         )
-        self.DISABLE_K_QUANT_MIXTURES = "k-quant मिश्रण को अक्षम करें और सभी टेंसर को एक ही प्रकार में क्वांटाइज़ करें"
+        self.USE_DATA_AS_IMPORTANCE_MATRIX = (
-        self.USE_DATA_AS_IMPORTANCE_MATRIX = "क्वांट अनुकूलन के लिए फ़ाइल में डेटा को महत्व मैट्रिक्स के रूप में उपयोग करें"
+            "क्वांट अनुकूलन के लिए फ़ाइल में डेटा को महत्व मैट्रिक्स के रूप में उपयोग करें"
-        self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
-            "इन टेंसर के लिए महत्व मैट्रिक्स का उपयोग करें"
         )
+        self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "इन टेंसर के लिए महत्व मैट्रिक्स का उपयोग करें"
         self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
             "इन टेंसर के लिए महत्व मैट्रिक्स का उपयोग न करें"
         )
@@ -1972,7 +1974,9 @@ def __init__(self):
         self.RESTART = "再起動"
         self.DELETE = "削除"
         self.CONFIRM_DELETION = "このタスクを削除してもよろしいですか？"
-        self.TASK_RUNNING_WARNING = "一部のタスクはまだ実行中です。終了してもよろしいですか？"
+        self.TASK_RUNNING_WARNING = (
+            "一部のタスクはまだ実行中です。終了してもよろしいですか？"
+        )
         self.YES = "はい"
         self.NO = "いいえ"
         self.DOWNLOAD_COMPLETE = "ダウンロード完了"
@@ -1985,11 +1989,11 @@ def __init__(self):
         self.NO_MODEL_SELECTED = "モデルが選択されていません"
         self.REFRESH_RELEASES = "リリースを更新"
         self.NO_SUITABLE_CUDA_BACKENDS = "適切なCUDAバックエンドが見つかりませんでした"
-        self.LLAMACPP_DOWNLOADED_EXTRACTED = (
+        self.LLAMACPP_DOWNLOADED_EXTRACTED = "llama.cppバイナリがダウンロードされ、{0}に抽出されました\nCUDAファイルは{1}に抽出されました"
-            "llama.cppバイナリがダウンロードされ、{0}に抽出されました\nCUDAファイルは{1}に抽出されました"
-        )
         self.CUDA_FILES_EXTRACTED = "CUDAファイルはに抽出されました"
-        self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = "抽出に適したCUDAバックエンドが見つかりませんでした"
+        self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = (
+            "抽出に適したCUDAバックエンドが見つかりませんでした"
+        )
         self.ERROR_FETCHING_RELEASES = "リリースの取得中にエラーが発生しました: {0}"
         self.CONFIRM_DELETION_TITLE = "削除の確認"
         self.LOG_FOR = "{0}のログ"
@@ -2014,10 +2018,10 @@ def __init__(self):
         self.RESTARTING_TASK = "タスクを再起動しています: {0}"
         self.IN_PROGRESS = "処理中"
         self.DOWNLOAD_FINISHED_EXTRACTED_TO = "ダウンロードが完了しました。抽出先: {0}"
-        self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = (
+        self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "llama.cppバイナリがダウンロードされ、{0}に抽出されました\nCUDAファイルは{1}に抽出されました"
-            "llama.cppバイナリがダウンロードされ、{0}に抽出されました\nCUDAファイルは{1}に抽出されました"
+        self.NO_SUITABLE_CUDA_BACKEND_FOUND = (
+            "抽出に適したCUDAバックエンドが見つかりませんでした"
         )
-        self.NO_SUITABLE_CUDA_BACKEND_FOUND = "抽出に適したCUDAバックエンドが見つかりませんでした"
         self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = (
             "llama.cppバイナリがダウンロードされ、{0}に抽出されました"
         )
@@ -2067,24 +2071,42 @@ def __init__(self):
         self.STARTING_IMATRIX_GENERATION = "IMatrixの生成を開始しています"
         self.BACKEND_PATH_NOT_EXIST = "バックエンドパスが存在しません: {0}"
         self.GENERATING_IMATRIX = "IMatrixを生成しています"
-        self.ERROR_STARTING_IMATRIX_GENERATION = "IMatrixの生成を開始中にエラーが発生しました: {0}"
+        self.ERROR_STARTING_IMATRIX_GENERATION = (
+            "IMatrixの生成を開始中にエラーが発生しました: {0}"
+        )
         self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix生成タスクが開始されました"
         self.ERROR_MESSAGE = "エラー: {0}"
         self.TASK_ERROR = "タスクエラー: {0}"
         self.APPLICATION_CLOSING = "アプリケーションを終了しています"
         self.APPLICATION_CLOSED = "アプリケーションが終了しました"
         self.SELECT_QUANTIZATION_TYPE = "量子化タイプを選択してください"
-        self.ALLOWS_REQUANTIZING = "すでに量子化されているテンソルの再量子化を許可します"
+        self.ALLOWS_REQUANTIZING = (
+            "すでに量子化されているテンソルの再量子化を許可します"
+        )
         self.LEAVE_OUTPUT_WEIGHT = "output.weightは（再）量子化されません"
-        self.DISABLE_K_QUANT_MIXTURES = "k-quant混合を無効にし、すべてのテンソルを同じタイプに量子化します"
+        self.DISABLE_K_QUANT_MIXTURES = (
-        self.USE_DATA_AS_IMPORTANCE_MATRIX = "量子化最適化の重要度マトリックスとしてファイル内のデータを使用します"
+            "k-quant混合を無効にし、すべてのテンソルを同じタイプに量子化します"
-        self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "これらのテンソルに重要度マトリックスを使用します"
+        )
-        self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = "これらのテンソルに重要度マトリックスを使用しません"
+        self.USE_DATA_AS_IMPORTANCE_MATRIX = (
+            "量子化最適化の重要度マトリックスとしてファイル内のデータを使用します"
+        )
+        self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
+            "これらのテンソルに重要度マトリックスを使用します"
+        )
+        self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
+            "これらのテンソルに重要度マトリックスを使用しません"
+        )
         self.OUTPUT_TENSOR_TYPE = "出力テンソルタイプ:"
-        self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "output.weightテンソルにこのタイプを使用します"
+        self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = (
+            "output.weightテンソルにこのタイプを使用します"
+        )
         self.TOKEN_EMBEDDING_TYPE = "トークン埋め込みタイプ:"
-        self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = "トークン埋め込みテンソルにこのタイプを使用します"
+        self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = (
-        self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = "入力と同じシャードで量子化されたモデルを生成します"
+            "トークン埋め込みテンソルにこのタイプを使用します"
+        )
+        self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = (
+            "入力と同じシャードで量子化されたモデルを生成します"
+        )
         self.OVERRIDE_MODEL_METADATA = "モデルメタデータを上書きする"
         self.INPUT_DATA_FILE_FOR_IMATRIX = "IMatrix生成用の入力データファイル"
         self.MODEL_TO_BE_QUANTIZED = "量子化されるモデル"
@@ -2741,11 +2763,11 @@ def __init__(self):
         self.NO_MODEL_SELECTED = "모델이 선택되지 않았습니다"
         self.REFRESH_RELEASES = "릴리스 새로 고침"
         self.NO_SUITABLE_CUDA_BACKENDS = "적합한 CUDA 백엔드를 찾을 수 없습니다"
-        self.LLAMACPP_DOWNLOADED_EXTRACTED = (
+        self.LLAMACPP_DOWNLOADED_EXTRACTED = "llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다.\nCUDA 파일이 {1}에 추출되었습니다."
-            "llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다.\nCUDA 파일이 {1}에 추출되었습니다."
-        )
         self.CUDA_FILES_EXTRACTED = "CUDA 파일이 에 추출되었습니다."
-        self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = "추출에 적합한 CUDA 백엔드를 찾을 수 없습니다."
+        self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = (
+            "추출에 적합한 CUDA 백엔드를 찾을 수 없습니다."
+        )
         self.ERROR_FETCHING_RELEASES = "릴리스를 가져오는 중 오류가 발생했습니다: {0}"
         self.CONFIRM_DELETION_TITLE = "삭제 확인"
         self.LOG_FOR = "{0}에 대한 로그"
@@ -2769,11 +2791,13 @@ def __init__(self):
         self.TASK_PRESET_SAVED_TO = "작업 프리셋이 {0}에 저장되었습니다."
         self.RESTARTING_TASK = "작업을 다시 시작하는 중입니다: {0}"
         self.IN_PROGRESS = "진행 중"
-        self.DOWNLOAD_FINISHED_EXTRACTED_TO = "다운로드가 완료되었습니다. 추출 위치: {0}"
+        self.DOWNLOAD_FINISHED_EXTRACTED_TO = (
-        self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = (
+            "다운로드가 완료되었습니다. 추출 위치: {0}"
-            "llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다.\nCUDA 파일이 {1}에 추출되었습니다."
+        )
+        self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다.\nCUDA 파일이 {1}에 추출되었습니다."
+        self.NO_SUITABLE_CUDA_BACKEND_FOUND = (
+            "추출에 적합한 CUDA 백엔드를 찾을 수 없습니다."
         )
-        self.NO_SUITABLE_CUDA_BACKEND_FOUND = "추출에 적합한 CUDA 백엔드를 찾을 수 없습니다."
         self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = (
             "llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다."
         )
@@ -2810,10 +2834,14 @@ def __init__(self):
         self.INPUT_FILE_NOT_EXIST = "입력 파일 '{0}'이 존재하지 않습니다."
         self.QUANTIZING_MODEL_TO = "{0}을 {1}(으)로 양자화하는 중입니다."
         self.QUANTIZATION_TASK_STARTED = "{0}에 대한 양자화 작업이 시작되었습니다."
-        self.ERROR_STARTING_QUANTIZATION = "양자화를 시작하는 중 오류가 발생했습니다: {0}"
+        self.ERROR_STARTING_QUANTIZATION = (
+            "양자화를 시작하는 중 오류가 발생했습니다: {0}"
+        )
         self.UPDATING_MODEL_INFO = "모델 정보를 업데이트하는 중입니다: {0}"
         self.TASK_FINISHED = "작업이 완료되었습니다: {0}"
-        self.SHOWING_TASK_DETAILS_FOR = "다음에 대한 작업 세부 정보를 표시하는 중입니다: {0}"
+        self.SHOWING_TASK_DETAILS_FOR = (
+            "다음에 대한 작업 세부 정보를 표시하는 중입니다: {0}"
+        )
         self.BROWSING_FOR_IMATRIX_DATA_FILE = "IMatrix 데이터 파일을 찾아보는 중입니다."
         self.SELECT_DATA_FILE = "데이터 파일 선택"
         self.BROWSING_FOR_IMATRIX_MODEL_FILE = "IMatrix 모델 파일을 찾아보는 중입니다."
@@ -2823,7 +2851,9 @@ def __init__(self):
         self.STARTING_IMATRIX_GENERATION = "IMatrix 생성을 시작하는 중입니다."
         self.BACKEND_PATH_NOT_EXIST = "백엔드 경로가 존재하지 않습니다: {0}"
         self.GENERATING_IMATRIX = "IMatrix를 생성하는 중입니다."
-        self.ERROR_STARTING_IMATRIX_GENERATION = "IMatrix 생성을 시작하는 중 오류가 발생했습니다: {0}"
+        self.ERROR_STARTING_IMATRIX_GENERATION = (
+            "IMatrix 생성을 시작하는 중 오류가 발생했습니다: {0}"
+        )
         self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix 생성 작업이 시작되었습니다."
         self.ERROR_MESSAGE = "오류: {0}"
         self.TASK_ERROR = "작업 오류: {0}"
@@ -2832,14 +2862,26 @@ def __init__(self):
         self.SELECT_QUANTIZATION_TYPE = "양자화 유형을 선택하세요."
         self.ALLOWS_REQUANTIZING = "이미 양자화된 텐서의 재양자화를 허용합니다."
         self.LEAVE_OUTPUT_WEIGHT = "output.weight를 (재)양자화하지 않은 상태로 둡니다."
-        self.DISABLE_K_QUANT_MIXTURES = "k-양자 혼합을 비활성화하고 모든 텐서를 동일한 유형으로 양자화합니다."
+        self.DISABLE_K_QUANT_MIXTURES = (
-        self.USE_DATA_AS_IMPORTANCE_MATRIX = "양자 최적화를 위한 중요도 행렬로 파일의 데이터를 사용합니다."
+            "k-양자 혼합을 비활성화하고 모든 텐서를 동일한 유형으로 양자화합니다."
-        self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "이러한 텐서에 중요도 행렬을 사용합니다."
+        )
-        self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = "이러한 텐서에 중요도 행렬을 사용하지 않습니다."
+        self.USE_DATA_AS_IMPORTANCE_MATRIX = (
+            "양자 최적화를 위한 중요도 행렬로 파일의 데이터를 사용합니다."
+        )
+        self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
+            "이러한 텐서에 중요도 행렬을 사용합니다."
+        )
+        self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
+            "이러한 텐서에 중요도 행렬을 사용하지 않습니다."
+        )
         self.OUTPUT_TENSOR_TYPE = "출력 텐서 유형:"
-        self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "output.weight 텐서에 이 유형을 사용합니다."
+        self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = (
+            "output.weight 텐서에 이 유형을 사용합니다."
+        )
         self.TOKEN_EMBEDDING_TYPE = "토큰 임베딩 유형:"
-        self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = "토큰 임베딩 텐서에 이 유형을 사용합니다."
+        self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = (
+            "토큰 임베딩 텐서에 이 유형을 사용합니다."
+        )
         self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = (
             "입력과 동일한 샤드에 양자화된 모델을 생성합니다."
         )
@@ -3794,9 +3836,7 @@ def __init__(self):
         self.STARTING_IMATRIX_GENERATION = "IMatrix জেনারেশন শুরু হচ্ছে"
         self.BACKEND_PATH_NOT_EXIST = "ব্যাকএন্ড পাথ বিদ্যমান নেই: {0}"
         self.GENERATING_IMATRIX = "IMatrix তৈরি করা হচ্ছে"
-        self.ERROR_STARTING_IMATRIX_GENERATION = (
+        self.ERROR_STARTING_IMATRIX_GENERATION = "IMatrix জেনারেশন শুরু করতে ত্রুটি: {0}"
-            "IMatrix জেনারেশন শুরু করতে ত্রুটি: {0}"
-        )
         self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix জেনারেশন টাস্ক শুরু হয়েছে"
         self.ERROR_MESSAGE = "ত্রুটি: {0}"
         self.TASK_ERROR = "টাস্ক ত্রুটি: {0}"
@@ -3804,11 +3844,13 @@ def __init__(self):
         self.APPLICATION_CLOSED = "অ্যাপ্লিকেশন বন্ধ"
         self.SELECT_QUANTIZATION_TYPE = "কোয়ান্টাইজেশন ধরণ নির্বাচন করুন"
         self.ALLOWS_REQUANTIZING = "যে টেন্সরগুলি ইতিমধ্যে কোয়ান্টাইজ করা হয়েছে তাদের পুনরায় কোয়ান্টাইজ করার অনুমতি দেয়"
-        self.LEAVE_OUTPUT_WEIGHT = (
+        self.LEAVE_OUTPUT_WEIGHT = "output.weight কে (পুনরায়) কোয়ান্টাইজ না করে রেখে দেবে"
-            "output.weight কে (পুনরায়) কোয়ান্টাইজ না করে রেখে দেবে"
+        self.DISABLE_K_QUANT_MIXTURES = (
+            "k-কোয়ান্ট মিশ্রণগুলি অক্ষম করুন এবং সমস্ত টেন্সরকে একই ধরণের কোয়ান্টাইজ করুন"
+        )
+        self.USE_DATA_AS_IMPORTANCE_MATRIX = (
+            "কোয়ান্ট অপ্টিমাইজেশনের জন্য ফাইলের ডেটা গুরুত্বপূর্ণ ম্যাট্রিক্স হিসাবে ব্যবহার করুন"
         )
-        self.DISABLE_K_QUANT_MIXTURES = "k-কোয়ান্ট মিশ্রণগুলি অক্ষম করুন এবং সমস্ত টেন্সরকে একই ধরণের কোয়ান্টাইজ করুন"
-        self.USE_DATA_AS_IMPORTANCE_MATRIX = "কোয়ান্ট অপ্টিমাইজেশনের জন্য ফাইলের ডেটা গুরুত্বপূর্ণ ম্যাট্রিক্স হিসাবে ব্যবহার করুন"
         self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
             "এই টেন্সরগুলির জন্য গুরুত্বপূর্ণ ম্যাট্রিক্স ব্যবহার করুন"
         )
@@ -5912,7 +5954,9 @@ def __init__(self):
             "llama.cpp 二進位檔案已下載並解壓縮至 {0}\nCUDA 檔案已解壓縮至 {1}"
         )
         self.NO_SUITABLE_CUDA_BACKEND_FOUND = "找不到合適的 CUDA 後端進行解壓縮"
-        self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = "llama.cpp 二進位檔案已下載並解壓縮至 {0}"
+        self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = (
+            "llama.cpp 二進位檔案已下載並解壓縮至 {0}"
+        )
         self.REFRESHING_LLAMACPP_RELEASES = "正在重新整理 llama.cpp 版本"
         self.UPDATING_ASSET_LIST = "正在更新資源清單"
         self.UPDATING_CUDA_OPTIONS = "正在更新 CUDA 選項"
@@ -5969,14 +6013,18 @@ def __init__(self):
         self.ALLOWS_REQUANTIZING = "允許重新量化已量化的張量"
         self.LEAVE_OUTPUT_WEIGHT = "將保留 output.weight 不被（重新）量化"
         self.DISABLE_K_QUANT_MIXTURES = "停用 k-quant 混合並將所有張量量化為相同類型"
-        self.USE_DATA_AS_IMPORTANCE_MATRIX = "使用檔案中的資料作為量化最佳化的重要性矩陣"
+        self.USE_DATA_AS_IMPORTANCE_MATRIX = (
+            "使用檔案中的資料作為量化最佳化的重要性矩陣"
+        )
         self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "對這些張量使用重要性矩陣"
         self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = "不要對這些張量使用重要性矩陣"
         self.OUTPUT_TENSOR_TYPE = "輸出張量類型："
         self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "對 output.weight 張量使用此類型"
         self.TOKEN_EMBEDDING_TYPE = "權杖嵌入類型："
         self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = "對權杖嵌入張量使用此類型"
-        self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = "將在與輸入相同的分片中產生量化模型"
+        self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = (
+            "將在與輸入相同的分片中產生量化模型"
+        )
         self.OVERRIDE_MODEL_METADATA = "覆蓋模型中繼資料"
         self.INPUT_DATA_FILE_FOR_IMATRIX = "IMatrix 產生的輸入資料檔案"
         self.MODEL_TO_BE_QUANTIZED = "要量化的模型"