from PyQt6.QtWidgets import * from PyQt6.QtCore import * from PyQt6.QtGui import * import os import sys import psutil import shutil import subprocess import time import signal import json import platform import requests import zipfile import re from datetime import datetime from imports_and_globals import ensure_directory, open_file_safe, resource_path from DownloadThread import DownloadThread from ModelInfoDialog import ModelInfoDialog from TaskListItem import TaskListItem from QuantizationThread import QuantizationThread from KVOverrideEntry import KVOverrideEntry from Logger import Logger from localizations import * class AutoGGUF(QMainWindow): def __init__(self): super().__init__() self.logger = Logger("AutoGGUF", "logs") self.logger.info(INITIALIZING_AUTOGGUF) self.setWindowTitle(WINDOW_TITLE) self.setWindowIcon(QIcon(resource_path("assets/favicon.ico"))) self.setGeometry(100, 100, 1600, 1200) ensure_directory(os.path.abspath("quantized_models")) main_layout = QHBoxLayout() left_layout = QVBoxLayout() right_layout = QVBoxLayout() # System info self.ram_bar = QProgressBar() self.cpu_label = QLabel(CPU_USAGE) left_layout.addWidget(QLabel(RAM_USAGE)) left_layout.addWidget(self.ram_bar) left_layout.addWidget(self.cpu_label) # Modify the backend selection backend_layout = QHBoxLayout() self.backend_combo = QComboBox() self.refresh_backends_button = QPushButton(REFRESH_BACKENDS) self.refresh_backends_button.clicked.connect(self.refresh_backends) backend_layout.addWidget(QLabel(BACKEND)) backend_layout.addWidget(self.backend_combo) backend_layout.addWidget(self.refresh_backends_button) left_layout.addLayout(backend_layout) # Modify the Download llama.cpp section download_group = QGroupBox(DOWNLOAD_LLAMACPP) download_layout = QFormLayout() self.release_combo = QComboBox() self.refresh_releases_button = QPushButton(REFRESH_RELEASES) self.refresh_releases_button.clicked.connect(self.refresh_releases) release_layout = QHBoxLayout() release_layout.addWidget(self.release_combo) release_layout.addWidget(self.refresh_releases_button) download_layout.addRow(SELECT_RELEASE, release_layout) self.asset_combo = QComboBox() self.asset_combo.currentIndexChanged.connect(self.update_cuda_option) download_layout.addRow(SELECT_ASSET, self.asset_combo) self.cuda_extract_checkbox = QCheckBox(EXTRACT_CUDA_FILES) self.cuda_extract_checkbox.setVisible(False) download_layout.addRow(self.cuda_extract_checkbox) self.cuda_backend_label = QLabel(SELECT_CUDA_BACKEND) self.cuda_backend_label.setVisible(False) self.backend_combo_cuda = QComboBox() self.backend_combo_cuda.setVisible(False) download_layout.addRow(self.cuda_backend_label, self.backend_combo_cuda) self.download_progress = QProgressBar() self.download_button = QPushButton(DOWNLOAD) self.download_button.clicked.connect(self.download_llama_cpp) download_layout.addRow(self.download_progress) download_layout.addRow(self.download_button) download_group.setLayout(download_layout) right_layout.addWidget(download_group) # Initialize releases and backends if os.environ.get("AUTOGGUF_CHECK_BACKEND", "").lower() == "enabled": self.refresh_releases() self.refresh_backends() # Models path models_layout = QHBoxLayout() self.models_input = QLineEdit(os.path.abspath("models")) models_button = QPushButton(BROWSE) models_button.clicked.connect(self.browse_models) models_layout.addWidget(QLabel(MODELS_PATH)) models_layout.addWidget(self.models_input) models_layout.addWidget(models_button) left_layout.addLayout(models_layout) # Output path output_layout = QHBoxLayout() self.output_input = QLineEdit(os.path.abspath("quantized_models")) output_button = QPushButton(BROWSE) output_button.clicked.connect(self.browse_output) output_layout.addWidget(QLabel(OUTPUT_PATH)) output_layout.addWidget(self.output_input) output_layout.addWidget(output_button) left_layout.addLayout(output_layout) # Logs path logs_layout = QHBoxLayout() self.logs_input = QLineEdit(os.path.abspath("logs")) logs_button = QPushButton(BROWSE) logs_button.clicked.connect(self.browse_logs) logs_layout.addWidget(QLabel(LOGS_PATH)) logs_layout.addWidget(self.logs_input) logs_layout.addWidget(logs_button) left_layout.addLayout(logs_layout) # Model list self.model_list = QListWidget() self.load_models() left_layout.addWidget(QLabel(AVAILABLE_MODELS)) left_layout.addWidget(self.model_list) # Refresh models button refresh_models_button = QPushButton(REFRESH_MODELS) refresh_models_button.clicked.connect(self.load_models) left_layout.addWidget(refresh_models_button) # Quantization options quant_options_scroll = QScrollArea() quant_options_widget = QWidget() quant_options_layout = QFormLayout() self.quant_type = QComboBox() self.quant_type.addItems( [ "IQ2_XXS", "IQ2_XS", "IQ2_S", "IQ2_M", "IQ1_S", "IQ1_M", "Q2_K", "Q2_K_S", "IQ3_XXS", "IQ3_S", "IQ3_M", "Q3_K", "IQ3_XS", "Q3_K_S", "Q3_K_M", "Q3_K_L", "IQ4_NL", "IQ4_XS", "Q4_K", "Q4_K_S", "Q4_K_M", "Q5_K", "Q5_K_S", "Q5_K_M", "Q6_K", "Q8_0", "Q4_0", "Q4_1", "Q5_0", "Q5_1", "Q4_0_4_4", "Q4_0_4_8", "Q4_0_8_8", "BF16", "F16", "F32", "COPY", ] ) quant_options_layout.addRow( self.create_label(QUANTIZATION_TYPE, SELECT_QUANTIZATION_TYPE), self.quant_type, ) self.allow_requantize = QCheckBox(ALLOW_REQUANTIZE) self.leave_output_tensor = QCheckBox(LEAVE_OUTPUT_TENSOR) self.pure = QCheckBox(PURE) quant_options_layout.addRow( self.create_label("", ALLOWS_REQUANTIZING), self.allow_requantize ) quant_options_layout.addRow( self.create_label("", LEAVE_OUTPUT_WEIGHT), self.leave_output_tensor ) quant_options_layout.addRow( self.create_label("", DISABLE_K_QUANT_MIXTURES), self.pure ) self.imatrix = QLineEdit() self.imatrix_button = QPushButton(BROWSE) self.imatrix_button.clicked.connect(self.browse_imatrix) imatrix_layout = QHBoxLayout() imatrix_layout.addWidget(self.imatrix) imatrix_layout.addWidget(self.imatrix_button) quant_options_layout.addRow( self.create_label(IMATRIX, USE_DATA_AS_IMPORTANCE_MATRIX), imatrix_layout ) self.include_weights = QLineEdit() self.exclude_weights = QLineEdit() quant_options_layout.addRow( self.create_label(INCLUDE_WEIGHTS, USE_IMPORTANCE_MATRIX_FOR_TENSORS), self.include_weights, ) quant_options_layout.addRow( self.create_label(EXCLUDE_WEIGHTS, DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS), self.exclude_weights, ) self.use_output_tensor_type = QCheckBox(USE_OUTPUT_TENSOR_TYPE) self.output_tensor_type = QComboBox() self.output_tensor_type.addItems( ["F32", "F16", "Q4_0", "Q4_1", "Q5_0", "Q5_1", "Q8_0"] ) self.output_tensor_type.setEnabled(False) self.use_output_tensor_type.toggled.connect( lambda checked: self.output_tensor_type.setEnabled(checked) ) output_tensor_layout = QHBoxLayout() output_tensor_layout.addWidget(self.use_output_tensor_type) output_tensor_layout.addWidget(self.output_tensor_type) quant_options_layout.addRow( self.create_label(OUTPUT_TENSOR_TYPE, USE_THIS_TYPE_FOR_OUTPUT_WEIGHT), output_tensor_layout, ) self.use_token_embedding_type = QCheckBox(USE_TOKEN_EMBEDDING_TYPE) self.token_embedding_type = QComboBox() self.token_embedding_type.addItems( ["F32", "F16", "Q4_0", "Q4_1", "Q5_0", "Q5_1", "Q8_0"] ) self.token_embedding_type.setEnabled(False) self.use_token_embedding_type.toggled.connect( lambda checked: self.token_embedding_type.setEnabled(checked) ) token_embedding_layout = QHBoxLayout() token_embedding_layout.addWidget(self.use_token_embedding_type) token_embedding_layout.addWidget(self.token_embedding_type) quant_options_layout.addRow( self.create_label(TOKEN_EMBEDDING_TYPE, USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS), token_embedding_layout, ) self.keep_split = QCheckBox(KEEP_SPLIT) self.override_kv = QLineEdit() quant_options_layout.addRow( self.create_label("", WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS), self.keep_split, ) # KV Override section self.kv_override_widget = QWidget() self.kv_override_layout = QVBoxLayout(self.kv_override_widget) self.kv_override_entries = [] add_override_button = QPushButton(ADD_NEW_OVERRIDE) add_override_button.clicked.connect(self.add_kv_override) kv_override_scroll = QScrollArea() kv_override_scroll.setWidgetResizable(True) kv_override_scroll.setWidget(self.kv_override_widget) kv_override_scroll.setMinimumHeight(200) kv_override_main_layout = QVBoxLayout() kv_override_main_layout.addWidget(kv_override_scroll) kv_override_main_layout.addWidget(add_override_button) quant_options_layout.addRow( self.create_label(KV_OVERRIDES, OVERRIDE_MODEL_METADATA), kv_override_main_layout, ) quant_options_widget.setLayout(quant_options_layout) quant_options_scroll.setWidget(quant_options_widget) quant_options_scroll.setWidgetResizable(True) left_layout.addWidget(quant_options_scroll) # Add this after the KV override section self.extra_arguments = QLineEdit() quant_options_layout.addRow( self.create_label(EXTRA_ARGUMENTS, "Additional command-line arguments"), self.extra_arguments, ) # Quantize button layout quantize_layout = QHBoxLayout() quantize_button = QPushButton(QUANTIZE_MODEL) quantize_button.clicked.connect(self.quantize_model) save_preset_button = QPushButton(SAVE_PRESET) save_preset_button.clicked.connect(self.save_preset) load_preset_button = QPushButton(LOAD_PRESET) load_preset_button.clicked.connect(self.load_preset) quantize_layout.addWidget(quantize_button) quantize_layout.addWidget(save_preset_button) quantize_layout.addWidget(load_preset_button) left_layout.addLayout(quantize_layout) # Task list self.task_list = QListWidget() self.task_list.setSelectionMode(QListWidget.SelectionMode.NoSelection) self.task_list.itemDoubleClicked.connect(self.show_task_details) left_layout.addWidget(QLabel(TASKS)) left_layout.addWidget(self.task_list) # IMatrix section imatrix_group = QGroupBox(IMATRIX_GENERATION) imatrix_layout = QFormLayout() self.imatrix_datafile = QLineEdit() self.imatrix_datafile_button = QPushButton(BROWSE) self.imatrix_datafile_button.clicked.connect(self.browse_imatrix_datafile) imatrix_datafile_layout = QHBoxLayout() imatrix_datafile_layout.addWidget(self.imatrix_datafile) imatrix_datafile_layout.addWidget(self.imatrix_datafile_button) imatrix_layout.addRow( self.create_label(DATA_FILE, INPUT_DATA_FILE_FOR_IMATRIX), imatrix_datafile_layout, ) self.imatrix_model = QLineEdit() self.imatrix_model_button = QPushButton(BROWSE) self.imatrix_model_button.clicked.connect(self.browse_imatrix_model) imatrix_model_layout = QHBoxLayout() imatrix_model_layout.addWidget(self.imatrix_model) imatrix_model_layout.addWidget(self.imatrix_model_button) imatrix_layout.addRow( self.create_label(MODEL, MODEL_TO_BE_QUANTIZED), imatrix_model_layout ) self.imatrix_output = QLineEdit() self.imatrix_output_button = QPushButton(BROWSE) self.imatrix_output_button.clicked.connect(self.browse_imatrix_output) imatrix_output_layout = QHBoxLayout() imatrix_output_layout.addWidget(self.imatrix_output) imatrix_output_layout.addWidget(self.imatrix_output_button) imatrix_layout.addRow( self.create_label(OUTPUT, OUTPUT_PATH_FOR_GENERATED_IMATRIX), imatrix_output_layout, ) self.imatrix_frequency = QSpinBox() self.imatrix_frequency.setRange(1, 100) # Set the range from 1 to 100 self.imatrix_frequency.setValue(1) # Set a default value imatrix_layout.addRow( self.create_label(OUTPUT_FREQUENCY, HOW_OFTEN_TO_SAVE_IMATRIX), self.imatrix_frequency, ) # Context size input (now a spinbox) self.imatrix_ctx_size = QSpinBox() self.imatrix_ctx_size.setRange(1, 1048576) # Up to one million tokens self.imatrix_ctx_size.setValue(512) # Set a default value imatrix_layout.addRow( self.create_label(CONTEXT_SIZE, CONTEXT_SIZE_FOR_IMATRIX), self.imatrix_ctx_size, ) # Threads input with slider and spinbox threads_layout = QHBoxLayout() self.threads_slider = QSlider(Qt.Orientation.Horizontal) self.threads_slider.setRange(1, 64) self.threads_slider.valueChanged.connect(self.update_threads_spinbox) self.threads_spinbox = QSpinBox() self.threads_spinbox.setRange(1, 128) self.threads_spinbox.valueChanged.connect(self.update_threads_slider) self.threads_spinbox.setMinimumWidth(75) threads_layout.addWidget(self.threads_slider) threads_layout.addWidget(self.threads_spinbox) imatrix_layout.addRow( self.create_label(THREADS, NUMBER_OF_THREADS_FOR_IMATRIX), threads_layout ) # GPU Offload for IMatrix (corrected version) gpu_offload_layout = QHBoxLayout() self.gpu_offload_slider = QSlider(Qt.Orientation.Horizontal) self.gpu_offload_slider.setRange(0, 200) self.gpu_offload_slider.valueChanged.connect(self.update_gpu_offload_spinbox) self.gpu_offload_spinbox = QSpinBox() self.gpu_offload_spinbox.setRange(0, 1000) self.gpu_offload_spinbox.valueChanged.connect(self.update_gpu_offload_slider) self.gpu_offload_spinbox.setMinimumWidth(75) self.gpu_offload_auto = QCheckBox(AUTO) self.gpu_offload_auto.stateChanged.connect(self.toggle_gpu_offload_auto) gpu_offload_layout.addWidget(self.gpu_offload_slider) gpu_offload_layout.addWidget(self.gpu_offload_spinbox) gpu_offload_layout.addWidget(self.gpu_offload_auto) imatrix_layout.addRow( self.create_label(GPU_OFFLOAD, SET_GPU_OFFLOAD_VALUE), gpu_offload_layout ) imatrix_generate_button = QPushButton(GENERATE_IMATRIX) imatrix_generate_button.clicked.connect(self.generate_imatrix) imatrix_layout.addRow(imatrix_generate_button) imatrix_group.setLayout(imatrix_layout) right_layout.addWidget(imatrix_group) main_widget = QWidget() main_layout.addLayout(left_layout, 2) main_layout.addLayout(right_layout, 1) main_widget.setLayout(main_layout) self.setCentralWidget(main_widget) # LoRA Conversion Section lora_group = QGroupBox(LORA_CONVERSION) lora_layout = QFormLayout() self.lora_input = QLineEdit() lora_input_button = QPushButton(BROWSE) lora_input_button.clicked.connect(self.browse_lora_input) lora_input_layout = QHBoxLayout() lora_input_layout.addWidget(self.lora_input) lora_input_layout.addWidget(lora_input_button) lora_layout.addRow( self.create_label(LORA_INPUT_PATH, SELECT_LORA_INPUT_DIRECTORY), lora_input_layout, ) self.lora_output = QLineEdit() lora_output_button = QPushButton(BROWSE) lora_output_button.clicked.connect(self.browse_lora_output) lora_output_layout = QHBoxLayout() lora_output_layout.addWidget(self.lora_output) lora_output_layout.addWidget(lora_output_button) lora_layout.addRow( self.create_label(LORA_OUTPUT_PATH, SELECT_LORA_OUTPUT_FILE), lora_output_layout, ) # Output Type Dropdown self.lora_output_type_combo = QComboBox() self.lora_output_type_combo.addItems(["GGML", "GGUF"]) self.lora_output_type_combo.currentIndexChanged.connect( self.update_base_model_visibility ) # Connect to update visibility lora_layout.addRow( self.create_label(OUTPUT_TYPE, SELECT_OUTPUT_TYPE), self.lora_output_type_combo, ) # Base Model Path (initially hidden) self.base_model_path = QLineEdit() base_model_button = QPushButton(BROWSE) base_model_button.clicked.connect(self.browse_base_model) base_model_layout = QHBoxLayout() base_model_layout.addWidget(self.base_model_path) base_model_layout.addWidget(base_model_button) self.base_model_widget = QWidget() self.base_model_widget.setLayout(base_model_layout) self.base_model_widget.setVisible(False) # Initially hidden lora_layout.addRow( self.create_label(BASE_MODEL, SELECT_BASE_MODEL_FILE), self.base_model_widget, ) lora_convert_button = QPushButton(CONVERT_LORA) lora_convert_button.clicked.connect(self.convert_lora) lora_layout.addRow(lora_convert_button) lora_group.setLayout(lora_layout) right_layout.addWidget(lora_group) # Export LoRA export_lora_group = QGroupBox(EXPORT_LORA) export_lora_layout = QFormLayout() self.export_lora_model = QLineEdit() export_lora_model_button = QPushButton(BROWSE) export_lora_model_button.clicked.connect(self.browse_export_lora_model) export_lora_model_layout = QHBoxLayout() export_lora_model_layout.addWidget(self.export_lora_model) export_lora_model_layout.addWidget(export_lora_model_button) export_lora_layout.addRow( self.create_label(MODEL, SELECT_MODEL_FILE), export_lora_model_layout ) self.export_lora_output = QLineEdit() export_lora_output_button = QPushButton(BROWSE) export_lora_output_button.clicked.connect(self.browse_export_lora_output) export_lora_output_layout = QHBoxLayout() export_lora_output_layout.addWidget(self.export_lora_output) export_lora_output_layout.addWidget(export_lora_output_button) export_lora_layout.addRow( self.create_label(OUTPUT, SELECT_OUTPUT_FILE), export_lora_output_layout ) # GGML LoRA Adapters self.export_lora_adapters = QListWidget() add_adapter_button = QPushButton(ADD_ADAPTER) add_adapter_button.clicked.connect(self.add_lora_adapter) adapters_layout = QVBoxLayout() adapters_layout.addWidget(self.export_lora_adapters) buttons_layout = QHBoxLayout() buttons_layout.addWidget(add_adapter_button) adapters_layout.addLayout(buttons_layout) export_lora_layout.addRow( self.create_label(GGML_LORA_ADAPTERS, SELECT_LORA_ADAPTER_FILES), adapters_layout, ) # Threads self.export_lora_threads = QSpinBox() self.export_lora_threads.setRange(1, 64) self.export_lora_threads.setValue(8) # Default value export_lora_layout.addRow( self.create_label(THREADS, NUMBER_OF_THREADS_FOR_LORA_EXPORT), self.export_lora_threads, ) export_lora_button = QPushButton(EXPORT_LORA) export_lora_button.clicked.connect(self.export_lora) export_lora_layout.addRow(export_lora_button) export_lora_group.setLayout(export_lora_layout) right_layout.addWidget( export_lora_group ) # Add the Export LoRA group to the right layout # Modify the task list to support right-click menu self.task_list.setContextMenuPolicy(Qt.ContextMenuPolicy.CustomContextMenu) self.task_list.customContextMenuRequested.connect(self.show_task_context_menu) # Timer for updating system info self.timer = QTimer() self.timer.timeout.connect(self.update_system_info) self.timer.start(200) # Initialize threads self.quant_threads = [] self.logger.info(AUTOGGUF_INITIALIZATION_COMPLETE) def refresh_backends(self): self.logger.info(REFRESHING_BACKENDS) llama_bin = os.path.abspath("llama_bin") if not os.path.exists(llama_bin): os.makedirs(llama_bin) self.backend_combo.clear() valid_backends = [] for item in os.listdir(llama_bin): item_path = os.path.join(llama_bin, item) if os.path.isdir(item_path) and "cudart-llama" not in item.lower(): valid_backends.append((item, item_path)) if valid_backends: for name, path in valid_backends: self.backend_combo.addItem(name, userData=path) self.backend_combo.setEnabled( True ) # Enable the combo box if there are valid backends else: self.backend_combo.addItem(NO_BACKENDS_AVAILABLE) self.backend_combo.setEnabled(False) self.logger.info(FOUND_VALID_BACKENDS.format(self.backend_combo.count())) def update_base_model_visibility(self, index): self.base_model_widget.setVisible( self.lora_output_type_combo.itemText(index) == "GGUF" ) def save_preset(self): self.logger.info(SAVING_PRESET) preset = { "quant_type": self.quant_type.currentText(), "allow_requantize": self.allow_requantize.isChecked(), "leave_output_tensor": self.leave_output_tensor.isChecked(), "pure": self.pure.isChecked(), "imatrix": self.imatrix.text(), "include_weights": self.include_weights.text(), "exclude_weights": self.exclude_weights.text(), "use_output_tensor_type": self.use_output_tensor_type.isChecked(), "output_tensor_type": self.output_tensor_type.currentText(), "use_token_embedding_type": self.use_token_embedding_type.isChecked(), "token_embedding_type": self.token_embedding_type.currentText(), "keep_split": self.keep_split.isChecked(), "kv_overrides": [ entry.get_override_string() for entry in self.kv_override_entries ], "extra_arguments": self.extra_arguments.text(), } file_name, _ = QFileDialog.getSaveFileName(self, SAVE_PRESET, "", JSON_FILES) if file_name: with open(file_name, "w") as f: json.dump(preset, f, indent=4) QMessageBox.information( self, PRESET_SAVED, PRESET_SAVED_TO.format(file_name) ) self.logger.info(PRESET_SAVED_TO.format(file_name)) def load_preset(self): self.logger.info(LOADING_PRESET) file_name, _ = QFileDialog.getOpenFileName(self, LOAD_PRESET, "", JSON_FILES) if file_name: try: with open(file_name, "r") as f: preset = json.load(f) self.quant_type.setCurrentText(preset.get("quant_type", "")) self.allow_requantize.setChecked(preset.get("allow_requantize", False)) self.leave_output_tensor.setChecked( preset.get("leave_output_tensor", False) ) self.pure.setChecked(preset.get("pure", False)) self.imatrix.setText(preset.get("imatrix", "")) self.include_weights.setText(preset.get("include_weights", "")) self.exclude_weights.setText(preset.get("exclude_weights", "")) self.use_output_tensor_type.setChecked( preset.get("use_output_tensor_type", False) ) self.output_tensor_type.setCurrentText( preset.get("output_tensor_type", "") ) self.use_token_embedding_type.setChecked( preset.get("use_token_embedding_type", False) ) self.token_embedding_type.setCurrentText( preset.get("token_embedding_type", "") ) self.keep_split.setChecked(preset.get("keep_split", False)) self.extra_arguments.setText(preset.get("extra_arguments", "")) # Clear existing KV overrides and add new ones for entry in self.kv_override_entries: self.remove_kv_override(entry) for override in preset.get("kv_overrides", []): self.add_kv_override(override) QMessageBox.information( self, PRESET_LOADED, PRESET_LOADED_FROM.format(file_name) ) except Exception as e: QMessageBox.critical(self, ERROR, FAILED_TO_LOAD_PRESET.format(str(e))) self.logger.info(PRESET_LOADED_FROM.format(file_name)) def save_task_preset(self, task_item): self.logger.info(SAVING_TASK_PRESET.format(task_item.task_name)) for thread in self.quant_threads: if thread.log_file == task_item.log_file: preset = { "command": thread.command, "backend_path": thread.cwd, "log_file": thread.log_file, } file_name, _ = QFileDialog.getSaveFileName( self, SAVE_TASK_PRESET, "", JSON_FILES ) if file_name: with open(file_name, "w") as f: json.dump(preset, f, indent=4) QMessageBox.information( self, TASK_PRESET_SAVED, TASK_PRESET_SAVED_TO.format(file_name) ) break def browse_export_lora_model(self): self.logger.info(BROWSING_FOR_EXPORT_LORA_MODEL_FILE) model_file, _ = QFileDialog.getOpenFileName( self, SELECT_MODEL_FILE, "", GGUF_FILES ) if model_file: self.export_lora_model.setText(os.path.abspath(model_file)) def browse_export_lora_output(self): self.logger.info(BROWSING_FOR_EXPORT_LORA_OUTPUT_FILE) output_file, _ = QFileDialog.getSaveFileName( self, SELECT_OUTPUT_FILE, "", GGUF_FILES ) if output_file: self.export_lora_output.setText(os.path.abspath(output_file)) def add_lora_adapter(self): self.logger.info(ADDING_LORA_ADAPTER) adapter_path, _ = QFileDialog.getOpenFileName( self, SELECT_LORA_ADAPTER_FILE, "", LORA_FILES ) if adapter_path: # Create a widget to hold the path and scale input adapter_widget = QWidget() adapter_layout = QHBoxLayout(adapter_widget) path_input = QLineEdit(adapter_path) path_input.setReadOnly(True) adapter_layout.addWidget(path_input) scale_input = QLineEdit("1.0") # Default scale value adapter_layout.addWidget(scale_input) delete_button = QPushButton(DELETE_ADAPTER) delete_button.clicked.connect( lambda: self.delete_lora_adapter_item(adapter_widget) ) adapter_layout.addWidget(delete_button) # Add the widget to the list list_item = QListWidgetItem(self.export_lora_adapters) list_item.setSizeHint(adapter_widget.sizeHint()) self.export_lora_adapters.addItem(list_item) self.export_lora_adapters.setItemWidget(list_item, adapter_widget) def browse_base_model(self): self.logger.info(BROWSING_FOR_BASE_MODEL_FOLDER) # Updated log message base_model_folder = QFileDialog.getExistingDirectory( self, SELECT_BASE_MODEL_FOLDER ) if base_model_folder: self.base_model_path.setText(os.path.abspath(base_model_folder)) def delete_lora_adapter_item(self, adapter_widget): self.logger.info(DELETING_LORA_ADAPTER) # Find the QListWidgetItem containing the adapter_widget for i in range(self.export_lora_adapters.count()): item = self.export_lora_adapters.item(i) if self.export_lora_adapters.itemWidget(item) == adapter_widget: self.export_lora_adapters.takeItem(i) # Remove the item break def export_lora(self): self.logger.info(STARTING_LORA_EXPORT) try: model_path = self.export_lora_model.text() output_path = self.export_lora_output.text() lora_adapters = [] for i in range(self.export_lora_adapters.count()): item = self.export_lora_adapters.item(i) adapter_widget = self.export_lora_adapters.itemWidget(item) path_input = adapter_widget.layout().itemAt(0).widget() scale_input = adapter_widget.layout().itemAt(1).widget() adapter_path = path_input.text() adapter_scale = scale_input.text() lora_adapters.append((adapter_path, adapter_scale)) if not model_path: raise ValueError(MODEL_PATH_REQUIRED) if not output_path: raise ValueError(OUTPUT_PATH_REQUIRED) if not lora_adapters: raise ValueError(AT_LEAST_ONE_LORA_ADAPTER_REQUIRED) backend_path = self.backend_combo.currentData() if not backend_path: raise ValueError(NO_BACKEND_SELECTED) command = [ os.path.join(backend_path, "llama-export-lora"), "--model", model_path, "--output", output_path, ] for adapter_path, adapter_scale in lora_adapters: if adapter_path: if adapter_scale: try: scale_value = float(adapter_scale) command.extend( ["--lora-scaled", adapter_path, str(scale_value)] ) except ValueError: raise ValueError(INVALID_LORA_SCALE_VALUE) else: command.extend(["--lora", adapter_path]) threads = self.export_lora_threads.value() command.extend(["--threads", str(threads)]) logs_path = self.logs_input.text() ensure_directory(logs_path) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") log_file = os.path.join(logs_path, f"lora_export_{timestamp}.log") thread = QuantizationThread(command, backend_path, log_file) self.quant_threads.append(thread) task_item = TaskListItem(EXPORTING_LORA, log_file, show_progress_bar=False) list_item = QListWidgetItem(self.task_list) list_item.setSizeHint(task_item.sizeHint()) self.task_list.addItem(list_item) self.task_list.setItemWidget(list_item, task_item) thread.status_signal.connect(task_item.update_status) thread.finished_signal.connect(lambda: self.task_finished(thread)) thread.error_signal.connect(lambda err: self.handle_error(err, task_item)) thread.start() self.logger.info(LORA_EXPORT_TASK_STARTED) except ValueError as e: self.show_error(str(e)) except Exception as e: self.show_error(ERROR_STARTING_LORA_EXPORT.format(str(e))) def restart_task(self, task_item): self.logger.info(RESTARTING_TASK.format(task_item.task_name)) for thread in self.quant_threads: if thread.log_file == task_item.log_file: new_thread = QuantizationThread( thread.command, thread.cwd, thread.log_file ) self.quant_threads.append(new_thread) new_thread.status_signal.connect(task_item.update_status) new_thread.finished_signal.connect( lambda: self.task_finished(new_thread) ) new_thread.error_signal.connect( lambda err: self.handle_error(err, task_item) ) new_thread.model_info_signal.connect(self.update_model_info) new_thread.start() task_item.update_status(IN_PROGRESS) break def browse_lora_input(self): self.logger.info(BROWSING_FOR_LORA_INPUT_DIRECTORY) lora_input_path = QFileDialog.getExistingDirectory( self, SELECT_LORA_INPUT_DIRECTORY ) if lora_input_path: self.lora_input.setText(os.path.abspath(lora_input_path)) ensure_directory(lora_input_path) def browse_lora_output(self): self.logger.info(BROWSING_FOR_LORA_OUTPUT_FILE) lora_output_file, _ = QFileDialog.getSaveFileName( self, SELECT_LORA_OUTPUT_FILE, "", GGUF_AND_BIN_FILES ) if lora_output_file: self.lora_output.setText(os.path.abspath(lora_output_file)) def convert_lora(self): self.logger.info(STARTING_LORA_CONVERSION) try: lora_input_path = self.lora_input.text() lora_output_path = self.lora_output.text() lora_output_type = self.lora_output_type_combo.currentText() if not lora_input_path: raise ValueError(LORA_INPUT_PATH_REQUIRED) if not lora_output_path: raise ValueError(LORA_OUTPUT_PATH_REQUIRED) if lora_output_type == "GGUF": # Use new file and parameters for GGUF command = [ "python", "src/convert_lora_to_gguf.py", "--outfile", lora_output_path, lora_input_path, ] base_model_path = self.base_model_path.text() if not base_model_path: raise ValueError(BASE_MODEL_PATH_REQUIRED) command.extend(["--base", base_model_path]) else: # Use old GGML parameters for GGML command = ["python", "src/convert_lora_to_ggml.py", lora_input_path] logs_path = self.logs_input.text() ensure_directory(logs_path) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") log_file = os.path.join(logs_path, f"lora_conversion_{timestamp}.log") thread = QuantizationThread(command, os.getcwd(), log_file) self.quant_threads.append(thread) task_name = LORA_CONVERSION_FROM_TO.format( os.path.basename(lora_input_path), os.path.basename(lora_output_path) ) task_item = TaskListItem(task_name, log_file, show_progress_bar=False) list_item = QListWidgetItem(self.task_list) list_item.setSizeHint(task_item.sizeHint()) self.task_list.addItem(list_item) self.task_list.setItemWidget(list_item, task_item) thread.status_signal.connect(task_item.update_status) thread.finished_signal.connect( lambda: self.lora_conversion_finished( thread, lora_input_path, lora_output_path ) ) thread.error_signal.connect(lambda err: self.handle_error(err, task_item)) thread.start() self.logger.info(LORA_CONVERSION_TASK_STARTED) except ValueError as e: self.show_error(str(e)) except Exception as e: self.show_error(ERROR_STARTING_LORA_CONVERSION.format(str(e))) def lora_conversion_finished(self, thread, input_path, output_path): self.logger.info(LORA_CONVERSION_FINISHED) if thread in self.quant_threads: self.quant_threads.remove(thread) try: # Only move the file if the output type is GGML if self.lora_output_type_combo.currentText() == "GGML": source_file = os.path.join(input_path, "ggml-adapter-model.bin") if os.path.exists(source_file): shutil.move(source_file, output_path) self.logger.info(LORA_FILE_MOVED.format(source_file, output_path)) else: self.logger.warning(LORA_FILE_NOT_FOUND.format(source_file)) except Exception as e: self.logger.error(ERROR_MOVING_LORA_FILE.format(str(e))) def download_finished(self, extract_dir): self.logger.info(DOWNLOAD_FINISHED_EXTRACTED_TO.format(extract_dir)) self.download_button.setEnabled(True) self.download_progress.setValue(100) if ( self.cuda_extract_checkbox.isChecked() and self.cuda_extract_checkbox.isVisible() ): cuda_backend = self.backend_combo_cuda.currentData() if cuda_backend and cuda_backend != NO_SUITABLE_CUDA_BACKENDS: self.extract_cuda_files(extract_dir, cuda_backend) QMessageBox.information( self, DOWNLOAD_COMPLETE, LLAMACPP_DOWNLOADED_AND_EXTRACTED.format(extract_dir, cuda_backend), ) else: QMessageBox.warning( self, CUDA_EXTRACTION_FAILED, NO_SUITABLE_CUDA_BACKEND_FOUND ) else: QMessageBox.information( self, DOWNLOAD_COMPLETE, LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED.format(extract_dir), ) self.refresh_backends() # Refresh the backends after successful download self.update_cuda_option() # Update CUDA options in case a CUDA-capable backend was downloaded # Select the newly downloaded backend new_backend_name = os.path.basename(extract_dir) index = self.backend_combo.findText(new_backend_name) if index >= 0: self.backend_combo.setCurrentIndex(index) def refresh_releases(self): self.logger.info(REFRESHING_LLAMACPP_RELEASES) try: response = requests.get( "https://api.github.com/repos/ggerganov/llama.cpp/releases" ) response.raise_for_status() # Raise an exception for bad status codes releases = response.json() self.release_combo.clear() for release in releases: self.release_combo.addItem(release["tag_name"], userData=release) self.release_combo.currentIndexChanged.connect(self.update_assets) self.update_assets() except requests.exceptions.RequestException as e: self.show_error(ERROR_FETCHING_RELEASES.format(str(e))) def update_assets(self): self.logger.debug(UPDATING_ASSET_LIST) self.asset_combo.clear() release = self.release_combo.currentData() if release: if "assets" in release: for asset in release["assets"]: self.asset_combo.addItem(asset["name"], userData=asset) else: self.show_error(NO_ASSETS_FOUND_FOR_RELEASE.format(release["tag_name"])) self.update_cuda_option() def download_llama_cpp(self): self.logger.info(STARTING_LLAMACPP_DOWNLOAD) asset = self.asset_combo.currentData() if not asset: self.show_error(NO_ASSET_SELECTED) return llama_bin = os.path.abspath("llama_bin") if not os.path.exists(llama_bin): os.makedirs(llama_bin) save_path = os.path.join(llama_bin, asset["name"]) self.download_thread = DownloadThread(asset["browser_download_url"], save_path) self.download_thread.progress_signal.connect(self.update_download_progress) self.download_thread.finished_signal.connect(self.download_finished) self.download_thread.error_signal.connect(self.download_error) self.download_thread.start() self.download_button.setEnabled(False) self.download_progress.setValue(0) def update_cuda_option(self): self.logger.debug(UPDATING_CUDA_OPTIONS) asset = self.asset_combo.currentData() # Handle the case where asset is None if asset is None: self.logger.warning(NO_ASSET_SELECTED_FOR_CUDA_CHECK) self.cuda_extract_checkbox.setVisible(False) self.cuda_backend_label.setVisible(False) self.backend_combo_cuda.setVisible(False) return # Exit the function early is_cuda = asset and "cudart" in asset["name"].lower() self.cuda_extract_checkbox.setVisible(is_cuda) self.cuda_backend_label.setVisible(is_cuda) self.backend_combo_cuda.setVisible(is_cuda) if is_cuda: self.update_cuda_backends() def update_cuda_backends(self): self.logger.debug(UPDATING_CUDA_BACKENDS) self.backend_combo_cuda.clear() llama_bin = os.path.abspath("llama_bin") if os.path.exists(llama_bin): for item in os.listdir(llama_bin): item_path = os.path.join(llama_bin, item) if os.path.isdir(item_path) and "cudart-llama" not in item.lower(): if "cu1" in item.lower(): # Only include CUDA-capable backends self.backend_combo_cuda.addItem(item, userData=item_path) if self.backend_combo_cuda.count() == 0: self.backend_combo_cuda.addItem(NO_SUITABLE_CUDA_BACKENDS) self.backend_combo_cuda.setEnabled(False) else: self.backend_combo_cuda.setEnabled(True) def update_download_progress(self, progress): self.download_progress.setValue(progress) def download_finished(self, extract_dir): self.download_button.setEnabled(True) self.download_progress.setValue(100) if ( self.cuda_extract_checkbox.isChecked() and self.cuda_extract_checkbox.isVisible() ): cuda_backend = self.backend_combo_cuda.currentData() if cuda_backend: self.extract_cuda_files(extract_dir, cuda_backend) QMessageBox.information( self, DOWNLOAD_COMPLETE, LLAMACPP_DOWNLOADED_AND_EXTRACTED.format(extract_dir, cuda_backend), ) else: QMessageBox.warning( self, CUDA_EXTRACTION_FAILED, NO_CUDA_BACKEND_SELECTED ) else: QMessageBox.information( self, DOWNLOAD_COMPLETE, LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED.format(extract_dir), ) self.refresh_backends() def extract_cuda_files(self, extract_dir, destination): self.logger.info(EXTRACTING_CUDA_FILES.format(extract_dir, destination)) for root, dirs, files in os.walk(extract_dir): for file in files: if file.lower().endswith(".dll"): source_path = os.path.join(root, file) dest_path = os.path.join(destination, file) shutil.copy2(source_path, dest_path) def download_error(self, error_message): self.logger.error(DOWNLOAD_ERROR.format(error_message)) self.download_button.setEnabled(True) self.download_progress.setValue(0) self.show_error(DOWNLOAD_FAILED.format(error_message)) # Clean up any partially downloaded files asset = self.asset_combo.currentData() if asset: partial_file = os.path.join(os.path.abspath("llama_bin"), asset["name"]) if os.path.exists(partial_file): os.remove(partial_file) def show_task_context_menu(self, position): self.logger.debug(SHOWING_TASK_CONTEXT_MENU) item = self.task_list.itemAt(position) if item is not None: context_menu = QMenu(self) properties_action = QAction(PROPERTIES, self) properties_action.triggered.connect(lambda: self.show_task_properties(item)) context_menu.addAction(properties_action) task_item = self.task_list.itemWidget(item) if task_item.status != COMPLETED: cancel_action = QAction(CANCEL, self) cancel_action.triggered.connect(lambda: self.cancel_task(item)) context_menu.addAction(cancel_action) if task_item.status == CANCELED: restart_action = QAction(RESTART, self) restart_action.triggered.connect(lambda: self.restart_task(task_item)) context_menu.addAction(restart_action) save_preset_action = QAction(SAVE_PRESET, self) save_preset_action.triggered.connect( lambda: self.save_task_preset(task_item) ) context_menu.addAction(save_preset_action) delete_action = QAction(DELETE, self) delete_action.triggered.connect(lambda: self.delete_task(item)) context_menu.addAction(delete_action) context_menu.exec(self.task_list.viewport().mapToGlobal(position)) def show_task_properties(self, item): self.logger.debug(SHOWING_PROPERTIES_FOR_TASK.format(item.text())) task_item = self.task_list.itemWidget(item) for thread in self.quant_threads: if thread.log_file == task_item.log_file: model_info_dialog = ModelInfoDialog(thread.model_info, self) model_info_dialog.exec() break def update_threads_spinbox(self, value): self.threads_spinbox.setValue(value) def update_threads_slider(self, value): self.threads_slider.setValue(value) def update_gpu_offload_spinbox(self, value): self.gpu_offload_spinbox.setValue(value) def update_gpu_offload_slider(self, value): self.gpu_offload_slider.setValue(value) def toggle_gpu_offload_auto(self, state): is_auto = state == Qt.CheckState.Checked self.gpu_offload_slider.setEnabled(not is_auto) self.gpu_offload_spinbox.setEnabled(not is_auto) def cancel_task(self, item): self.logger.info(CANCELLING_TASK.format(item.text())) task_item = self.task_list.itemWidget(item) for thread in self.quant_threads: if thread.log_file == task_item.log_file: thread.terminate() task_item.update_status(CANCELED) break def retry_task(self, item): task_item = self.task_list.itemWidget(item) # TODO: Implement the logic to restart the task pass def delete_task(self, item): self.logger.info(DELETING_TASK.format(item.text())) reply = QMessageBox.question( self, CONFIRM_DELETION_TITLE, CONFIRM_DELETION, QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No, QMessageBox.StandardButton.No, ) if reply == QMessageBox.StandardButton.Yes: row = self.task_list.row(item) self.task_list.takeItem(row) # If the task is still running, terminate it task_item = self.task_list.itemWidget(item) for thread in self.quant_threads: if thread.log_file == task_item.log_file: thread.terminate() self.quant_threads.remove(thread) break def create_label(self, text, tooltip): label = QLabel(text) label.setToolTip(tooltip) return label def load_models(self): self.logger.info(LOADING_MODELS) models_dir = self.models_input.text() ensure_directory(models_dir) self.model_list.clear() for file in os.listdir(models_dir): if file.endswith(".gguf"): self.model_list.addItem(file) self.logger.info(LOADED_MODELS.format(self.model_list.count())) def browse_models(self): self.logger.info(BROWSING_FOR_MODELS_DIRECTORY) models_path = QFileDialog.getExistingDirectory(self, SELECT_MODELS_DIRECTORY) if models_path: self.models_input.setText(os.path.abspath(models_path)) ensure_directory(models_path) self.load_models() def browse_output(self): self.logger.info(BROWSING_FOR_OUTPUT_DIRECTORY) output_path = QFileDialog.getExistingDirectory(self, SELECT_OUTPUT_DIRECTORY) if output_path: self.output_input.setText(os.path.abspath(output_path)) ensure_directory(output_path) def browse_logs(self): self.logger.info(BROWSING_FOR_LOGS_DIRECTORY) logs_path = QFileDialog.getExistingDirectory(self, SELECT_LOGS_DIRECTORY) if logs_path: self.logs_input.setText(os.path.abspath(logs_path)) ensure_directory(logs_path) def browse_imatrix(self): self.logger.info(BROWSING_FOR_IMATRIX_FILE) imatrix_file, _ = QFileDialog.getOpenFileName( self, SELECT_IMATRIX_FILE, "", DAT_FILES ) if imatrix_file: self.imatrix.setText(os.path.abspath(imatrix_file)) def update_system_info(self): ram = psutil.virtual_memory() cpu = psutil.cpu_percent() self.ram_bar.setValue(int(ram.percent)) self.ram_bar.setFormat( RAM_USAGE_FORMAT.format( ram.percent, ram.used // 1024 // 1024, ram.total // 1024 // 1024 ) ) self.cpu_label.setText(CPU_USAGE_FORMAT.format(cpu)) def validate_quantization_inputs(self): self.logger.debug(VALIDATING_QUANTIZATION_INPUTS) errors = [] if not self.backend_combo.currentData(): errors.append(NO_BACKEND_SELECTED) if not self.models_input.text(): errors.append(MODELS_PATH_REQUIRED) if not self.output_input.text(): errors.append(OUTPUT_PATH_REQUIRED) if not self.logs_input.text(): errors.append(LOGS_PATH_REQUIRED) if not self.model_list.currentItem(): errors.append(NO_MODEL_SELECTED) if errors: raise ValueError("\n".join(errors)) def add_kv_override(self, override_string=None): entry = KVOverrideEntry() entry.deleted.connect(self.remove_kv_override) if override_string: key, value = override_string.split("=") type_, val = value.split(":") entry.key_input.setText(key) entry.type_combo.setCurrentText(type_) entry.value_input.setText(val) self.kv_override_layout.addWidget(entry) self.kv_override_entries.append(entry) def remove_kv_override(self, entry): self.kv_override_layout.removeWidget(entry) self.kv_override_entries.remove(entry) entry.deleteLater() def quantize_model(self): self.logger.info(STARTING_MODEL_QUANTIZATION) try: self.validate_quantization_inputs() selected_model = self.model_list.currentItem() if not selected_model: raise ValueError(NO_MODEL_SELECTED) model_name = selected_model.text() backend_path = self.backend_combo.currentData() if not backend_path: raise ValueError(NO_BACKEND_SELECTED) quant_type = self.quant_type.currentText() input_path = os.path.join(self.models_input.text(), model_name) model_name = selected_model.text() quant_type = self.quant_type.currentText() # Start building the output name output_name_parts = [ os.path.splitext(model_name)[0], "converted", quant_type, ] # Check for output tensor options if ( self.use_output_tensor_type.isChecked() or self.leave_output_tensor.isChecked() ): output_tensor_part = "o" if self.use_output_tensor_type.isChecked(): output_tensor_part += "." + self.output_tensor_type.currentText() output_name_parts.append(output_tensor_part) # Check for embedding tensor options if self.use_token_embedding_type.isChecked(): embd_tensor_part = "t." + self.token_embedding_type.currentText() output_name_parts.append(embd_tensor_part) # Check for pure option if self.pure.isChecked(): output_name_parts.append("pure") # Check for requantize option if self.allow_requantize.isChecked(): output_name_parts.append("rq") # Check for KV override 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")] if self.allow_requantize.isChecked(): command.append("--allow-requantize") if self.leave_output_tensor.isChecked(): command.append("--leave-output-tensor") if self.pure.isChecked(): command.append("--pure") if self.imatrix.text(): command.extend(["--imatrix", self.imatrix.text()]) if self.include_weights.text(): command.extend(["--include-weights", self.include_weights.text()]) if self.exclude_weights.text(): command.extend(["--exclude-weights", self.exclude_weights.text()]) if self.use_output_tensor_type.isChecked(): command.extend( ["--output-tensor-type", self.output_tensor_type.currentText()] ) if self.use_token_embedding_type.isChecked(): command.extend( ["--token-embedding-type", self.token_embedding_type.currentText()] ) if self.keep_split.isChecked(): command.append("--keep-split") if self.override_kv.text(): for entry in self.kv_override_entries: override_string = entry.get_override_string() if override_string: command.extend(["--override-kv", override_string]) command.extend([input_path, output_path, quant_type]) # Add extra arguments if self.extra_arguments.text(): command.extend(self.extra_arguments.text().split()) logs_path = self.logs_input.text() ensure_directory(logs_path) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") log_file = os.path.join( logs_path, f"{model_name}_{timestamp}_{quant_type}.log" ) thread = QuantizationThread(command, backend_path, log_file) self.quant_threads.append(thread) task_item = TaskListItem( QUANTIZING_MODEL_TO.format(model_name, quant_type), log_file ) list_item = QListWidgetItem(self.task_list) list_item.setSizeHint(task_item.sizeHint()) self.task_list.addItem(list_item) self.task_list.setItemWidget(list_item, task_item) # Connect the output signal to the new progress parsing function thread.output_signal.connect(lambda line: self.parse_progress(line, task_item)) thread.status_signal.connect(task_item.update_status) thread.finished_signal.connect(lambda: self.task_finished(thread)) thread.error_signal.connect(lambda err: self.handle_error(err, task_item)) thread.model_info_signal.connect(self.update_model_info) thread.start() self.logger.info(QUANTIZATION_TASK_STARTED.format(model_name)) except ValueError as e: self.show_error(str(e)) except Exception as e: self.show_error(ERROR_STARTING_QUANTIZATION.format(str(e))) def update_model_info(self, model_info): self.logger.debug(UPDATING_MODEL_INFO.format(model_info)) # TODO: Do something with this pass def parse_progress(self, line, task_item): # Parses the output line for progress information and updates the task item. match = re.search(r"\[(\d+)/(\d+)\]", line) if match: current = int(match.group(1)) total = int(match.group(2)) progress = int((current / total) * 100) task_item.update_progress(progress) def task_finished(self, thread): self.logger.info(TASK_FINISHED.format(thread.log_file)) if thread in self.quant_threads: self.quant_threads.remove(thread) def show_task_details(self, item): self.logger.debug(SHOWING_TASK_DETAILS_FOR.format(item.text())) task_item = self.task_list.itemWidget(item) if task_item: log_dialog = QDialog(self) log_dialog.setWindowTitle(LOG_FOR.format(task_item.task_name)) log_dialog.setGeometry(200, 200, 800, 600) log_text = QPlainTextEdit() log_text.setReadOnly(True) layout = QVBoxLayout() layout.addWidget(log_text) log_dialog.setLayout(layout) # Load existing content if os.path.exists(task_item.log_file): with open_file_safe(task_item.log_file, "r") as f: log_text.setPlainText(f.read()) # Connect to the thread if it's still running for thread in self.quant_threads: if thread.log_file == task_item.log_file: thread.output_signal.connect(log_text.appendPlainText) break log_dialog.exec() def browse_imatrix_datafile(self): self.logger.info(BROWSING_FOR_IMATRIX_DATA_FILE) datafile, _ = QFileDialog.getOpenFileName(self, SELECT_DATA_FILE, "", ALL_FILES) if datafile: self.imatrix_datafile.setText(os.path.abspath(datafile)) def browse_imatrix_model(self): self.logger.info(BROWSING_FOR_IMATRIX_MODEL_FILE) model_file, _ = QFileDialog.getOpenFileName( self, SELECT_MODEL_FILE, "", GGUF_FILES ) if model_file: self.imatrix_model.setText(os.path.abspath(model_file)) def browse_imatrix_output(self): self.logger.info(BROWSING_FOR_IMATRIX_OUTPUT_FILE) output_file, _ = QFileDialog.getSaveFileName( self, SELECT_OUTPUT_FILE, "", DAT_FILES ) if output_file: self.imatrix_output.setText(os.path.abspath(output_file)) def update_gpu_offload_spinbox(self, value): self.gpu_offload_spinbox.setValue(value) def update_gpu_offload_slider(self, value): self.gpu_offload_slider.setValue(value) def toggle_gpu_offload_auto(self, state): is_auto = state == Qt.CheckState.Checked self.gpu_offload_slider.setEnabled(not is_auto) self.gpu_offload_spinbox.setEnabled(not is_auto) def generate_imatrix(self): self.logger.info(STARTING_IMATRIX_GENERATION) try: backend_path = self.backend_combo.currentData() if not os.path.exists(backend_path): raise FileNotFoundError(BACKEND_PATH_NOT_EXIST.format(backend_path)) # Check if the Model area is empty if not self.imatrix_model.text(): raise ValueError(MODEL_PATH_REQUIRED_FOR_IMATRIX) command = [ os.path.join(backend_path, "llama-imatrix"), "-f", self.imatrix_datafile.text(), "-m", self.imatrix_model.text(), "-o", self.imatrix_output.text(), "--output-frequency", str(self.imatrix_frequency.value()), "--ctx-size", str(self.imatrix_ctx_size.value()), "--threads", str(self.threads_spinbox.value()), ] if self.gpu_offload_auto.isChecked(): command.extend(["-ngl", "99"]) elif self.gpu_offload_spinbox.value() > 0: command.extend(["-ngl", str(self.gpu_offload_spinbox.value())]) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") log_file = os.path.join(self.logs_input.text(), f"imatrix_{timestamp}.log") thread = QuantizationThread(command, backend_path, log_file) self.quant_threads.append(thread) task_name = GENERATING_IMATRIX_FOR.format( os.path.basename(self.imatrix_model.text()) ) task_item = TaskListItem(task_name, log_file, show_progress_bar=False) list_item = QListWidgetItem(self.task_list) list_item.setSizeHint(task_item.sizeHint()) self.task_list.addItem(list_item) self.task_list.setItemWidget(list_item, task_item) thread.status_signal.connect(task_item.update_status) thread.finished_signal.connect(lambda: self.task_finished(thread)) thread.error_signal.connect(lambda err: self.handle_error(err, task_item)) thread.start() except Exception as e: self.show_error(ERROR_STARTING_IMATRIX_GENERATION.format(str(e))) self.logger.info(IMATRIX_GENERATION_TASK_STARTED) 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): self.logger.error(TASK_ERROR.format(error_message)) self.show_error(error_message) task_item.set_error() def closeEvent(self, event: QCloseEvent): self.logger.info(APPLICATION_CLOSING) if self.quant_threads: reply = QMessageBox.question( self, WARNING, TASK_RUNNING_WARNING, QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No, QMessageBox.StandardButton.No, ) if reply == QMessageBox.StandardButton.Yes: for thread in self.quant_threads: thread.terminate() event.accept() else: event.ignore() else: event.accept() self.logger.info(APPLICATION_CLOSED) if __name__ == "__main__": app = QApplication(sys.argv) window = AutoGGUF() window.show() sys.exit(app.exec())