diff --git a/src/AutoGGUF.py b/src/AutoGGUF.py index e09664f..86a2c63 100644 --- a/src/AutoGGUF.py +++ b/src/AutoGGUF.py @@ -837,15 +837,15 @@ def delete_lora_adapter_item(self, adapter_widget): break def browse_hf_model_input(self): - self.logger.info("Browsing for HuggingFace model directory") + self.logger.info(BROWSE_FOR_HF_MODEL_DIRECTORY) model_dir = QFileDialog.getExistingDirectory(self, SELECT_HF_MODEL_DIRECTORY) if model_dir: self.hf_model_input.setText(os.path.abspath(model_dir)) def browse_hf_outfile(self): - self.logger.info("Browsing for HuggingFace to GGUF output file") + self.logger.info(BROWSE_FOR_HF_TO_GGUF_OUTPUT) outfile, _ = QFileDialog.getSaveFileName( - self, "Select Output File", "", "GGUF Files (*.gguf)" + self, SELECT_OUTPUT_FILE, "", GGUF_FILES ) if outfile: self.hf_outfile.setText(os.path.abspath(outfile)) @@ -1425,7 +1425,7 @@ def load_models(self): # Add sharded models for base_name, shards in sharded_models.items(): parent_item = QTreeWidgetItem(self.model_tree) - parent_item.setText(0, f"{base_name} (sharded)") + parent_item.setText(0, f"{base_name} ({SHARDED})") # Sort shards by shard number and get the first one first_shard = sorted(shards, key=lambda x: x[0])[0][1] parent_item.setData(0, Qt.ItemDataRole.UserRole, first_shard) diff --git a/src/localizations.py b/src/localizations.py index dc357f1..f78bc5e 100644 --- a/src/localizations.py +++ b/src/localizations.py @@ -3,275 +3,7 @@ class _Localization: def __init__(self): - super().__init__() - - # General UI - self.WINDOW_TITLE = "" - self.RAM_USAGE = "" - self.CPU_USAGE = "" - self.BACKEND = "" - self.REFRESH_BACKENDS = "" - self.MODELS_PATH = "" - self.OUTPUT_PATH = "" - self.LOGS_PATH = "" - self.BROWSE = "" - self.AVAILABLE_MODELS = "" - self.REFRESH_MODELS = "" - - # Quantization - self.QUANTIZATION_TYPE = "" - self.ALLOW_REQUANTIZE = "" - self.LEAVE_OUTPUT_TENSOR = "" - self.PURE = "" - self.IMATRIX = "" - self.INCLUDE_WEIGHTS = "" - self.EXCLUDE_WEIGHTS = "" - self.USE_OUTPUT_TENSOR_TYPE = "" - self.USE_TOKEN_EMBEDDING_TYPE = "" - self.KEEP_SPLIT = "" - self.KV_OVERRIDES = "" - self.ADD_NEW_OVERRIDE = "" - self.QUANTIZE_MODEL = "" - self.EXTRA_ARGUMENTS = "" - self.EXTRA_ARGUMENTS_LABEL = "" - self.QUANTIZATION_COMMAND = "" - - # Presets - self.SAVE_PRESET = "" - self.LOAD_PRESET = "" - - # Tasks - self.TASKS = "" - - # llama.cpp Download - self.DOWNLOAD_LLAMACPP = "" - self.SELECT_RELEASE = "" - self.SELECT_ASSET = "" - self.EXTRACT_CUDA_FILES = "" - self.SELECT_CUDA_BACKEND = "" - self.DOWNLOAD = "" - self.REFRESH_RELEASES = "" - - # IMatrix Generation - self.IMATRIX_GENERATION = "" - self.DATA_FILE = "" - self.MODEL = "" - self.OUTPUT = "" - self.OUTPUT_FREQUENCY = "" - self.GPU_OFFLOAD = "" - self.AUTO = "" - self.GENERATE_IMATRIX = "" - self.CONTEXT_SIZE = "" - self.CONTEXT_SIZE_FOR_IMATRIX = "" - self.THREADS = "" - self.NUMBER_OF_THREADS_FOR_IMATRIX = "" - self.IMATRIX_GENERATION_COMMAND = "" - - # LoRA Conversion - self.LORA_CONVERSION = "" - self.LORA_INPUT_PATH = "" - self.LORA_OUTPUT_PATH = "" - self.SELECT_LORA_INPUT_DIRECTORY = "" - self.SELECT_LORA_OUTPUT_FILE = "" - self.CONVERT_LORA = "" - self.LORA_CONVERSION_COMMAND = "" - - # LoRA Export - self.EXPORT_LORA = "" - self.GGML_LORA_ADAPTERS = "" - self.SELECT_LORA_ADAPTER_FILES = "" - self.ADD_ADAPTER = "" - self.DELETE_ADAPTER = "" - self.LORA_SCALE = "" - self.ENTER_LORA_SCALE_VALUE = "" - self.NUMBER_OF_THREADS_FOR_LORA_EXPORT = "" - self.LORA_EXPORT_COMMAND = "" - - # HuggingFace to GGUF Conversion - self.HF_TO_GGUF_CONVERSION = "" - self.MODEL_DIRECTORY = "" - self.OUTPUT_FILE = "" - self.OUTPUT_TYPE = "" - self.VOCAB_ONLY = "" - self.USE_TEMP_FILE = "" - self.NO_LAZY_EVALUATION = "" - self.MODEL_NAME = "" - self.VERBOSE = "" - self.SPLIT_MAX_SIZE = "" - self.DRY_RUN = "" - self.CONVERT_HF_TO_GGUF = "" - self.SELECT_HF_MODEL_DIRECTORY = "" - - # General Messages - self.ERROR = "" - self.WARNING = "" - self.PROPERTIES = "" - self.CANCEL = "" - self.RESTART = "" - self.DELETE = "" - self.CONFIRM_DELETION = "" - self.TASK_RUNNING_WARNING = "" - self.YES = "" - self.NO = "" - self.COMPLETED = "" - - # File Types - self.ALL_FILES = "" - self.GGUF_FILES = "" - self.DAT_FILES = "" - self.JSON_FILES = "" - self.BIN_FILES = "" - self.LORA_FILES = "" - self.GGUF_AND_BIN_FILES = "" - - # Status Messages - self.DOWNLOAD_COMPLETE = "" - self.CUDA_EXTRACTION_FAILED = "" - self.PRESET_SAVED = "" - self.PRESET_LOADED = "" - self.NO_ASSET_SELECTED = "" - self.DOWNLOAD_FAILED = "" - self.NO_BACKEND_SELECTED = "" - self.NO_MODEL_SELECTED = "" - self.NO_SUITABLE_CUDA_BACKENDS = "" - self.IN_PROGRESS = "" - self.LLAMACPP_DOWNLOADED_EXTRACTED = "" - self.CUDA_FILES_EXTRACTED = "" - self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = "" - self.ERROR_FETCHING_RELEASES = "" - self.CONFIRM_DELETION_TITLE = "" - self.LOG_FOR = "" - self.FAILED_LOAD_PRESET = "" - self.INITIALIZING_AUTOGGUF = "" - self.AUTOGGUF_INITIALIZATION_COMPLETE = "" - self.REFRESHING_BACKENDS = "" - self.NO_BACKENDS_AVAILABLE = "" - self.FOUND_VALID_BACKENDS = "" - self.SAVING_PRESET = "" - self.PRESET_SAVED_TO = "" - self.LOADING_PRESET = "" - self.PRESET_LOADED_FROM = "" - self.ADDING_KV_OVERRIDE = "" - self.SAVING_TASK_PRESET = "" - self.TASK_PRESET_SAVED = "" - self.TASK_PRESET_SAVED_TO = "" - self.RESTARTING_TASK = "" - self.DOWNLOAD_FINISHED_EXTRACTED_TO = "" - self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "" - self.NO_SUITABLE_CUDA_BACKEND_FOUND = "" - self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = "" - self.REFRESHING_LLAMACPP_RELEASES = "" - self.UPDATING_ASSET_LIST = "" - self.UPDATING_CUDA_OPTIONS = "" - self.STARTING_LLAMACPP_DOWNLOAD = "" - self.UPDATING_CUDA_BACKENDS = "" - self.NO_CUDA_BACKEND_SELECTED = "" - self.EXTRACTING_CUDA_FILES = "" - self.DOWNLOAD_ERROR = "" - self.SHOWING_TASK_CONTEXT_MENU = "" - self.SHOWING_PROPERTIES_FOR_TASK = "" - self.CANCELLING_TASK = "" - self.CANCELED = "" - self.DELETING_TASK = "" - self.LOADING_MODELS = "" - self.LOADED_MODELS = "" - self.BROWSING_FOR_MODELS_DIRECTORY = "" - self.SELECT_MODELS_DIRECTORY = "" - self.BROWSING_FOR_OUTPUT_DIRECTORY = "" - self.SELECT_OUTPUT_DIRECTORY = "" - self.BROWSING_FOR_LOGS_DIRECTORY = "" - self.SELECT_LOGS_DIRECTORY = "" - self.BROWSING_FOR_IMATRIX_FILE = "" - self.SELECT_IMATRIX_FILE = "" - self.RAM_USAGE_FORMAT = "" - self.CPU_USAGE_FORMAT = "" - self.VALIDATING_QUANTIZATION_INPUTS = "" - self.MODELS_PATH_REQUIRED = "" - self.OUTPUT_PATH_REQUIRED = "" - self.LOGS_PATH_REQUIRED = "" - self.STARTING_MODEL_QUANTIZATION = "" - self.INPUT_FILE_NOT_EXIST = "" - self.QUANTIZING_MODEL_TO = "" - self.QUANTIZATION_TASK_STARTED = "" - self.ERROR_STARTING_QUANTIZATION = "" - self.UPDATING_MODEL_INFO = "" - self.TASK_FINISHED = "" - self.SHOWING_TASK_DETAILS_FOR = "" - self.BROWSING_FOR_IMATRIX_DATA_FILE = "" - self.SELECT_DATA_FILE = "" - self.BROWSING_FOR_IMATRIX_MODEL_FILE = "" - self.SELECT_MODEL_FILE = "" - self.BROWSING_FOR_IMATRIX_OUTPUT_FILE = "" - self.SELECT_OUTPUT_FILE = "" - self.STARTING_IMATRIX_GENERATION = "" - self.BACKEND_PATH_NOT_EXIST = "" - self.GENERATING_IMATRIX = "" - self.ERROR_STARTING_IMATRIX_GENERATION = "" - self.IMATRIX_GENERATION_TASK_STARTED = "" - self.ERROR_MESSAGE = "" - self.TASK_ERROR = "" - self.APPLICATION_CLOSING = "" - self.APPLICATION_CLOSED = "" - self.SELECT_QUANTIZATION_TYPE = "" - self.ALLOWS_REQUANTIZING = "" - self.LEAVE_OUTPUT_WEIGHT = "" - self.DISABLE_K_QUANT_MIXTURES = "" - self.USE_DATA_AS_IMPORTANCE_MATRIX = "" - self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "" - self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = "" - self.OUTPUT_TENSOR_TYPE = "" - self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "" - self.TOKEN_EMBEDDING_TYPE = "" - self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = "" - self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = "" - self.OVERRIDE_MODEL_METADATA = "" - self.INPUT_DATA_FILE_FOR_IMATRIX = "" - self.MODEL_TO_BE_QUANTIZED = "" - self.OUTPUT_PATH_FOR_GENERATED_IMATRIX = "" - self.HOW_OFTEN_TO_SAVE_IMATRIX = "" - self.SET_GPU_OFFLOAD_VALUE = "" - self.STARTING_LORA_CONVERSION = "" - self.LORA_INPUT_PATH_REQUIRED = "" - self.LORA_OUTPUT_PATH_REQUIRED = "" - self.ERROR_STARTING_LORA_CONVERSION = "" - self.LORA_CONVERSION_TASK_STARTED = "" - self.BROWSING_FOR_LORA_INPUT_DIRECTORY = "" - self.BROWSING_FOR_LORA_OUTPUT_FILE = "" - self.CONVERTING_LORA = "" - self.LORA_CONVERSION_FINISHED = "" - self.LORA_FILE_MOVED = "" - self.LORA_FILE_NOT_FOUND = "" - self.ERROR_MOVING_LORA_FILE = "" - self.MODEL_PATH_REQUIRED = "" - self.AT_LEAST_ONE_LORA_ADAPTER_REQUIRED = "" - self.INVALID_LORA_SCALE_VALUE = "" - self.ERROR_STARTING_LORA_EXPORT = "" - self.LORA_EXPORT_TASK_STARTED = "" - self.EXPORTING_LORA = "" - self.BROWSING_FOR_EXPORT_LORA_MODEL_FILE = "" - self.BROWSING_FOR_EXPORT_LORA_OUTPUT_FILE = "" - self.ADDING_LORA_ADAPTER = "" - self.DELETING_LORA_ADAPTER = "" - self.SELECT_LORA_ADAPTER_FILE = "" - self.STARTING_LORA_EXPORT = "" - self.SELECT_OUTPUT_TYPE = "" - self.BASE_MODEL = "" - self.SELECT_BASE_MODEL_FILE = "" - self.BASE_MODEL_PATH_REQUIRED = "" - self.BROWSING_FOR_BASE_MODEL_FILE = "" - self.SELECT_BASE_MODEL_FOLDER = "" - self.BROWSING_FOR_BASE_MODEL_FOLDER = "" - self.LORA_CONVERSION_FROM_TO = "" - self.GENERATING_IMATRIX_FOR = "" - self.MODEL_PATH_REQUIRED_FOR_IMATRIX = "" - self.NO_ASSET_SELECTED_FOR_CUDA_CHECK = "" - self.NO_QUANTIZATION_TYPE_SELECTED = "" - self.STARTING_HF_TO_GGUF_CONVERSION = "" - self.MODEL_DIRECTORY_REQUIRED = "" - self.HF_TO_GGUF_CONVERSION_COMMAND = "" - self.CONVERTING_TO_GGUF = "" - self.ERROR_STARTING_HF_TO_GGUF_CONVERSION = "" - self.HF_TO_GGUF_CONVERSION_TASK_STARTED = "" + pass class _English(_Localization): @@ -374,6 +106,10 @@ def __init__(self): self.DRY_RUN = "Dry Run" self.CONVERT_HF_TO_GGUF = "Convert HF to GGUF" self.SELECT_HF_MODEL_DIRECTORY = "Select HuggingFace Model Directory" + self.BROWSE_FOR_HF_MODEL_DIRECTORY = "Browsing for HuggingFace model directory" + self.BROWSE_FOR_HF_TO_GGUF_OUTPUT = ( + "Browsing for HuggingFace to GGUF output file" + ) # General Messages self.ERROR = "Error" @@ -398,6 +134,7 @@ def __init__(self): self.BIN_FILES = "Binary Files (*.bin)" self.LORA_FILES = "LoRA Files (*.bin)" self.GGUF_AND_BIN_FILES = "GGUF and Binary Files (*.gguf *.bin)" + self.SHARDED = "sharded" # Status Messages self.DOWNLOAD_COMPLETE = "Download Complete" @@ -591,50 +328,118 @@ def __init__(self): ) -class _French: - # French localization +class _French(_Localization): def __init__(self): super().__init__() - self.WINDOW_TITLE = "AutoGGUF (quantificateur automatisé de modèles GGUF)" + + # Interface utilisateur générale + self.WINDOW_TITLE = "AutoGGUF (quantificateur automatique de modèles GGUF)" self.RAM_USAGE = "Utilisation RAM :" self.CPU_USAGE = "Utilisation CPU :" self.BACKEND = "Backend Llama.cpp :" - self.REFRESH_BACKENDS = "Actualiser les Backends" - self.MODELS_PATH = "Chemin des Modèles :" - self.OUTPUT_PATH = "Chemin de Sortie :" - self.LOGS_PATH = "Chemin des Logs :" + self.REFRESH_BACKENDS = "Rafraîchir les backends" + self.MODELS_PATH = "Chemin des modèles :" + self.OUTPUT_PATH = "Chemin de sortie :" + self.LOGS_PATH = "Chemin des logs :" self.BROWSE = "Parcourir" - self.AVAILABLE_MODELS = "Modèles Disponibles :" - self.QUANTIZATION_TYPE = "Type de Quantification :" - self.ALLOW_REQUANTIZE = "Autoriser la Requantification" - self.LEAVE_OUTPUT_TENSOR = "Laisser le Tenseur de Sortie" + self.AVAILABLE_MODELS = "Modèles disponibles :" + self.REFRESH_MODELS = "Rafraîchir les modèles" + + # Quantification + self.QUANTIZATION_TYPE = "Type de quantification :" + self.ALLOW_REQUANTIZE = "Autoriser la requantification" + self.LEAVE_OUTPUT_TENSOR = "Laisser le tenseur de sortie" self.PURE = "Pur" self.IMATRIX = "IMatrix :" - self.INCLUDE_WEIGHTS = "Inclure les Poids :" - self.EXCLUDE_WEIGHTS = "Exclure les Poids :" - self.USE_OUTPUT_TENSOR_TYPE = "Utiliser le Type de Tenseur de Sortie" - self.USE_TOKEN_EMBEDDING_TYPE = "Utiliser le Type d'Embedding de Token" - self.KEEP_SPLIT = "Garder la Division" + self.INCLUDE_WEIGHTS = "Inclure les poids :" + self.EXCLUDE_WEIGHTS = "Exclure les poids :" + self.USE_OUTPUT_TENSOR_TYPE = "Utiliser le type de tenseur de sortie" + self.USE_TOKEN_EMBEDDING_TYPE = "Utiliser le type d'embedding de token" + self.KEEP_SPLIT = "Garder la séparation" self.KV_OVERRIDES = "Remplacements KV :" self.ADD_NEW_OVERRIDE = "Ajouter un nouveau remplacement" - self.QUANTIZE_MODEL = "Quantifier le Modèle" - self.SAVE_PRESET = "Sauvegarder le Préréglage" - self.LOAD_PRESET = "Charger le Préréglage" + self.QUANTIZE_MODEL = "Quantifier le modèle" + self.EXTRA_ARGUMENTS = "Arguments supplémentaires :" + self.EXTRA_ARGUMENTS_LABEL = "Arguments de ligne de commande supplémentaires" + self.QUANTIZATION_COMMAND = "Commande de quantification" + + # Préréglages + self.SAVE_PRESET = "Sauvegarder le préréglage" + self.LOAD_PRESET = "Charger le préréglage" + + # Tâches self.TASKS = "Tâches :" + + # Téléchargement de llama.cpp self.DOWNLOAD_LLAMACPP = "Télécharger llama.cpp" - self.SELECT_RELEASE = "Sélectionner la Version :" - self.SELECT_ASSET = "Sélectionner l'Asset :" + self.SELECT_RELEASE = "Sélectionner la version :" + self.SELECT_ASSET = "Sélectionner l'asset :" self.EXTRACT_CUDA_FILES = "Extraire les fichiers CUDA" - self.SELECT_CUDA_BACKEND = "Sélectionner le Backend CUDA :" + self.SELECT_CUDA_BACKEND = "Sélectionner le backend CUDA :" self.DOWNLOAD = "Télécharger" + self.REFRESH_RELEASES = "Rafraîchir les versions" + + # Génération IMatrix self.IMATRIX_GENERATION = "Génération IMatrix" - self.DATA_FILE = "Fichier de Données :" + self.DATA_FILE = "Fichier de données :" self.MODEL = "Modèle :" self.OUTPUT = "Sortie :" - self.OUTPUT_FREQUENCY = "Fréquence de Sortie :" + self.OUTPUT_FREQUENCY = "Fréquence de sortie :" self.GPU_OFFLOAD = "Déchargement GPU :" self.AUTO = "Auto" self.GENERATE_IMATRIX = "Générer IMatrix" + self.CONTEXT_SIZE = "Taille du contexte :" + self.CONTEXT_SIZE_FOR_IMATRIX = "Taille du contexte pour la génération IMatrix" + self.THREADS = "Threads :" + self.NUMBER_OF_THREADS_FOR_IMATRIX = ( + "Nombre de threads pour la génération IMatrix" + ) + self.IMATRIX_GENERATION_COMMAND = "Commande de génération IMatrix" + + # Conversion LoRA + self.LORA_CONVERSION = "Conversion LoRA" + self.LORA_INPUT_PATH = "Chemin d'entrée LoRA" + self.LORA_OUTPUT_PATH = "Chemin de sortie LoRA" + self.SELECT_LORA_INPUT_DIRECTORY = "Sélectionner le répertoire d'entrée LoRA" + self.SELECT_LORA_OUTPUT_FILE = "Sélectionner le fichier de sortie LoRA" + self.CONVERT_LORA = "Convertir LoRA" + self.LORA_CONVERSION_COMMAND = "Commande de conversion LoRA" + + # Export LoRA + self.EXPORT_LORA = "Exporter LoRA" + self.GGML_LORA_ADAPTERS = "Adaptateurs LoRA GGML" + self.SELECT_LORA_ADAPTER_FILES = "Sélectionner les fichiers d'adaptateur LoRA" + self.ADD_ADAPTER = "Ajouter un adaptateur" + self.DELETE_ADAPTER = "Supprimer" + self.LORA_SCALE = "Échelle LoRA" + self.ENTER_LORA_SCALE_VALUE = "Entrer la valeur d'échelle LoRA (Optionnel)" + self.NUMBER_OF_THREADS_FOR_LORA_EXPORT = "Nombre de threads pour l'export LoRA" + self.LORA_EXPORT_COMMAND = "Commande d'export LoRA" + + # Conversion HuggingFace vers GGUF + self.HF_TO_GGUF_CONVERSION = "Conversion HuggingFace vers GGUF" + self.MODEL_DIRECTORY = "Répertoire du modèle :" + self.OUTPUT_FILE = "Fichier de sortie :" + self.OUTPUT_TYPE = "Type de sortie :" + self.VOCAB_ONLY = "Vocabulaire uniquement" + self.USE_TEMP_FILE = "Utiliser un fichier temporaire" + self.NO_LAZY_EVALUATION = "Pas d'évaluation paresseuse" + self.MODEL_NAME = "Nom du modèle :" + self.VERBOSE = "Verbeux" + self.SPLIT_MAX_SIZE = "Taille max de séparation :" + self.DRY_RUN = "Simulation" + self.CONVERT_HF_TO_GGUF = "Convertir HF vers GGUF" + self.SELECT_HF_MODEL_DIRECTORY = ( + "Sélectionner le répertoire du modèle HuggingFace" + ) + self.BROWSE_FOR_HF_MODEL_DIRECTORY = ( + "Parcourir pour le répertoire du modèle HuggingFace" + ) + self.BROWSE_FOR_HF_TO_GGUF_OUTPUT = ( + "Parcourir pour le fichier de sortie HuggingFace vers GGUF" + ) + + # Messages généraux self.ERROR = "Erreur" self.WARNING = "Avertissement" self.PROPERTIES = "Propriétés" @@ -647,17 +452,32 @@ def __init__(self): ) self.YES = "Oui" self.NO = "Non" - self.DOWNLOAD_COMPLETE = "Téléchargement Terminé" - self.CUDA_EXTRACTION_FAILED = "Échec de l'Extraction CUDA" - self.PRESET_SAVED = "Préréglage Sauvegardé" - self.PRESET_LOADED = "Préréglage Chargé" + self.COMPLETED = "Terminé" + + # Types de fichiers + self.ALL_FILES = "Tous les fichiers (*)" + self.GGUF_FILES = "Fichiers GGUF (*.gguf)" + self.DAT_FILES = "Fichiers DAT (*.dat)" + self.JSON_FILES = "Fichiers JSON (*.json)" + self.BIN_FILES = "Fichiers binaires (*.bin)" + self.LORA_FILES = "Fichiers LoRA (*.bin)" + self.GGUF_AND_BIN_FILES = "Fichiers GGUF et binaires (*.gguf *.bin)" + self.SHARDED = "fragmenté" + + # Messages de statut + self.DOWNLOAD_COMPLETE = "Téléchargement terminé" + self.CUDA_EXTRACTION_FAILED = "Échec de l'extraction CUDA" + self.PRESET_SAVED = "Préréglage sauvegardé" + self.PRESET_LOADED = "Préréglage chargé" self.NO_ASSET_SELECTED = "Aucun asset sélectionné" self.DOWNLOAD_FAILED = "Échec du téléchargement" self.NO_BACKEND_SELECTED = "Aucun backend sélectionné" self.NO_MODEL_SELECTED = "Aucun modèle sélectionné" - self.REFRESH_RELEASES = "Actualiser les Versions" self.NO_SUITABLE_CUDA_BACKENDS = "Aucun backend CUDA approprié trouvé" - self.LLAMACPP_DOWNLOADED_EXTRACTED = "Binaire llama.cpp téléchargé et extrait vers {0}\nFichiers CUDA extraits vers {1}" + self.IN_PROGRESS = "En cours" + self.LLAMACPP_DOWNLOADED_EXTRACTED = ( + "Binaire llama.cpp téléchargé et extrait vers {0}" + ) self.CUDA_FILES_EXTRACTED = "Fichiers CUDA extraits vers" self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = ( "Aucun backend CUDA approprié trouvé pour l'extraction" @@ -665,39 +485,36 @@ def __init__(self): self.ERROR_FETCHING_RELEASES = ( "Erreur lors de la récupération des versions : {0}" ) - self.CONFIRM_DELETION_TITLE = "Confirmer la Suppression" + self.CONFIRM_DELETION_TITLE = "Confirmer la suppression" self.LOG_FOR = "Log pour {0}" - self.ALL_FILES = "Tous les Fichiers (*)" - self.GGUF_FILES = "Fichiers GGUF (*.gguf)" - self.DAT_FILES = "Fichiers DAT (*.dat)" - self.JSON_FILES = "Fichiers JSON (*.json)" self.FAILED_LOAD_PRESET = "Échec du chargement du préréglage : {0}" self.INITIALIZING_AUTOGGUF = "Initialisation de l'application AutoGGUF" self.AUTOGGUF_INITIALIZATION_COMPLETE = "Initialisation d'AutoGGUF terminée" - self.REFRESHING_BACKENDS = "Actualisation des backends" + self.REFRESHING_BACKENDS = "Rafraîchissement des backends" self.NO_BACKENDS_AVAILABLE = "Aucun backend disponible" self.FOUND_VALID_BACKENDS = "{0} backends valides trouvés" self.SAVING_PRESET = "Sauvegarde du préréglage" - self.PRESET_SAVED_TO = "Préréglage sauvegardé dans {0}" + self.PRESET_SAVED_TO = "Préréglage sauvegardé vers {0}" self.LOADING_PRESET = "Chargement du préréglage" self.PRESET_LOADED_FROM = "Préréglage chargé depuis {0}" self.ADDING_KV_OVERRIDE = "Ajout du remplacement KV : {0}" self.SAVING_TASK_PRESET = "Sauvegarde du préréglage de tâche pour {0}" - self.TASK_PRESET_SAVED = "Préréglage de Tâche Sauvegardé" - self.TASK_PRESET_SAVED_TO = "Préréglage de tâche sauvegardé dans {0}" + self.TASK_PRESET_SAVED = "Préréglage de tâche sauvegardé" + self.TASK_PRESET_SAVED_TO = "Préréglage de tâche sauvegardé vers {0}" self.RESTARTING_TASK = "Redémarrage de la tâche : {0}" - self.IN_PROGRESS = "En Cours" self.DOWNLOAD_FINISHED_EXTRACTED_TO = ( "Téléchargement terminé. Extrait vers : {0}" ) - self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "Binaire llama.cpp téléchargé et extrait vers {0}\nFichiers CUDA extraits vers {1}" + self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = ( + "Binaire llama.cpp téléchargé et extrait vers {0}" + ) self.NO_SUITABLE_CUDA_BACKEND_FOUND = ( "Aucun backend CUDA approprié trouvé pour l'extraction" ) self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = ( "Binaire llama.cpp téléchargé et extrait vers {0}" ) - self.REFRESHING_LLAMACPP_RELEASES = "Actualisation des versions de llama.cpp" + self.REFRESHING_LLAMACPP_RELEASES = "Rafraîchissement des versions llama.cpp" self.UPDATING_ASSET_LIST = "Mise à jour de la liste des assets" self.UPDATING_CUDA_OPTIONS = "Mise à jour des options CUDA" self.STARTING_LLAMACPP_DOWNLOAD = "Démarrage du téléchargement de llama.cpp" @@ -716,14 +533,14 @@ def __init__(self): self.DELETING_TASK = "Suppression de la tâche : {0}" self.LOADING_MODELS = "Chargement des modèles" self.LOADED_MODELS = "{0} modèles chargés" - self.BROWSING_FOR_MODELS_DIRECTORY = "Recherche du répertoire des modèles" - self.SELECT_MODELS_DIRECTORY = "Sélectionner le Répertoire des Modèles" - self.BROWSING_FOR_OUTPUT_DIRECTORY = "Recherche du répertoire de sortie" - self.SELECT_OUTPUT_DIRECTORY = "Sélectionner le Répertoire de Sortie" - self.BROWSING_FOR_LOGS_DIRECTORY = "Recherche du répertoire des logs" - self.SELECT_LOGS_DIRECTORY = "Sélectionner le Répertoire des Logs" - self.BROWSING_FOR_IMATRIX_FILE = "Recherche du fichier IMatrix" - self.SELECT_IMATRIX_FILE = "Sélectionner le Fichier IMatrix" + self.BROWSING_FOR_MODELS_DIRECTORY = "Parcourir pour le répertoire des modèles" + self.SELECT_MODELS_DIRECTORY = "Sélectionner le répertoire des modèles" + self.BROWSING_FOR_OUTPUT_DIRECTORY = "Parcourir pour le répertoire de sortie" + self.SELECT_OUTPUT_DIRECTORY = "Sélectionner le répertoire de sortie" + self.BROWSING_FOR_LOGS_DIRECTORY = "Parcourir pour le répertoire des logs" + self.SELECT_LOGS_DIRECTORY = "Sélectionner le répertoire des logs" + self.BROWSING_FOR_IMATRIX_FILE = "Parcourir pour le fichier IMatrix" + self.SELECT_IMATRIX_FILE = "Sélectionner le fichier IMatrix" self.RAM_USAGE_FORMAT = "{0:.1f}% ({1} Mo / {2} Mo)" self.CPU_USAGE_FORMAT = "Utilisation CPU : {0:.1f}%" self.VALIDATING_QUANTIZATION_INPUTS = "Validation des entrées de quantification" @@ -735,29 +552,35 @@ def __init__(self): self.QUANTIZING_MODEL_TO = "Quantification de {0} vers {1}" self.QUANTIZATION_TASK_STARTED = "Tâche de quantification démarrée pour {0}" self.ERROR_STARTING_QUANTIZATION = ( - "Erreur au démarrage de la quantification : {0}" + "Erreur lors du démarrage de la quantification : {0}" ) - self.UPDATING_MODEL_INFO = "Mise à jour des infos du modèle : {0}" + self.UPDATING_MODEL_INFO = "Mise à jour des informations du modèle : {0}" self.TASK_FINISHED = "Tâche terminée : {0}" self.SHOWING_TASK_DETAILS_FOR = "Affichage des détails de la tâche pour : {0}" - self.BROWSING_FOR_IMATRIX_DATA_FILE = "Recherche du fichier de données IMatrix" - self.SELECT_DATA_FILE = "Sélectionner le Fichier de Données" - self.BROWSING_FOR_IMATRIX_MODEL_FILE = "Recherche du fichier modèle IMatrix" - self.SELECT_MODEL_FILE = "Sélectionner le Fichier Modèle" - self.BROWSING_FOR_IMATRIX_OUTPUT_FILE = "Recherche du fichier de sortie IMatrix" - self.SELECT_OUTPUT_FILE = "Sélectionner le Fichier de Sortie" + self.BROWSING_FOR_IMATRIX_DATA_FILE = ( + "Parcourir pour le fichier de données IMatrix" + ) + self.SELECT_DATA_FILE = "Sélectionner le fichier de données" + self.BROWSING_FOR_IMATRIX_MODEL_FILE = ( + "Parcourir pour le fichier de modèle IMatrix" + ) + self.SELECT_MODEL_FILE = "Sélectionner le fichier de modèle" + self.BROWSING_FOR_IMATRIX_OUTPUT_FILE = ( + "Parcourir pour le fichier de sortie IMatrix" + ) + self.SELECT_OUTPUT_FILE = "Sélectionner le fichier de sortie" self.STARTING_IMATRIX_GENERATION = "Démarrage de la génération IMatrix" self.BACKEND_PATH_NOT_EXIST = "Le chemin du backend n'existe pas : {0}" self.GENERATING_IMATRIX = "Génération de l'IMatrix" self.ERROR_STARTING_IMATRIX_GENERATION = ( - "Erreur au démarrage de la génération IMatrix : {0}" + "Erreur lors du démarrage de la génération IMatrix : {0}" ) self.IMATRIX_GENERATION_TASK_STARTED = "Tâche de génération IMatrix démarrée" self.ERROR_MESSAGE = "Erreur : {0}" self.TASK_ERROR = "Erreur de tâche : {0}" self.APPLICATION_CLOSING = "Fermeture de l'application" self.APPLICATION_CLOSED = "Application fermée" - self.SELECT_QUANTIZATION_TYPE = "Sélectionnez le type de quantification" + self.SELECT_QUANTIZATION_TYPE = "Sélectionner le type de quantification" self.ALLOWS_REQUANTIZING = "Permet de requantifier les tenseurs déjà quantifiés" self.LEAVE_OUTPUT_WEIGHT = "Laissera output.weight non (re)quantifié" self.DISABLE_K_QUANT_MIXTURES = ( @@ -765,21 +588,21 @@ def __init__(self): ) self.USE_DATA_AS_IMPORTANCE_MATRIX = "Utilise les données du fichier comme matrice d'importance pour les optimisations de quant" self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = ( - "Utiliser la matrice d'importance pour ces tenseurs" + "Utilise la matrice d'importance pour ces tenseurs" ) self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = ( - "Ne pas utiliser la matrice d'importance pour ces tenseurs" + "N'utilise pas la matrice d'importance pour ces tenseurs" ) - self.OUTPUT_TENSOR_TYPE = "Type de Tenseur de Sortie :" + self.OUTPUT_TENSOR_TYPE = "Type de tenseur de sortie :" self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = ( - "Utiliser ce type pour le tenseur output.weight" + "Utilise ce type pour le tenseur output.weight" ) - self.TOKEN_EMBEDDING_TYPE = "Type d'Embedding de Token :" + self.TOKEN_EMBEDDING_TYPE = "Type d'embedding de token :" self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = ( - "Utiliser ce type pour le tenseur des embeddings de token" + "Utilise ce type pour le tenseur d'embeddings de token" ) self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = ( - "Générera le modèle quantifié dans les mêmes shards que l'entrée" + "Générera le modèle quantifié dans les mêmes fragments que l'entrée" ) self.OVERRIDE_MODEL_METADATA = "Remplacer les métadonnées du modèle" self.INPUT_DATA_FILE_FOR_IMATRIX = ( @@ -791,25 +614,6 @@ def __init__(self): ) self.HOW_OFTEN_TO_SAVE_IMATRIX = "Fréquence de sauvegarde de l'IMatrix" self.SET_GPU_OFFLOAD_VALUE = "Définir la valeur de déchargement GPU (-ngl)" - self.COMPLETED = "Terminé" - self.REFRESH_MODELS = "Actualiser les modèles" - self.REFRESH_MODELS = "Actualiser les modèles" - self.EXTRA_ARGUMENTS = "Arguments supplémentaires :" - self.EXTRA_ARGUMENTS_LABEL = "Arguments supplémentaires en ligne de commande" - self.CONTEXT_SIZE = "Taille du contexte :" - self.CONTEXT_SIZE_FOR_IMATRIX = ( - "Taille du contexte pour la génération d'IMatrix" - ) - self.THREADS = "Threads :" - self.NUMBER_OF_THREADS_FOR_IMATRIX = ( - "Nombre de threads pour la génération d'IMatrix" - ) - self.LORA_CONVERSION = "Conversion LoRA" - self.LORA_INPUT_PATH = "Chemin d'entrée LoRA" - self.LORA_OUTPUT_PATH = "Chemin de sortie LoRA" - self.SELECT_LORA_INPUT_DIRECTORY = "Sélectionner le répertoire d'entrée LoRA" - self.SELECT_LORA_OUTPUT_FILE = "Sélectionner le fichier de sortie LoRA" - self.CONVERT_LORA = "Convertir LoRA" self.STARTING_LORA_CONVERSION = "Démarrage de la conversion LoRA" self.LORA_INPUT_PATH_REQUIRED = "Le chemin d'entrée LoRA est requis." self.LORA_OUTPUT_PATH_REQUIRED = "Le chemin de sortie LoRA est requis." @@ -817,73 +621,80 @@ def __init__(self): "Erreur lors du démarrage de la conversion LoRA : {}" ) self.LORA_CONVERSION_TASK_STARTED = "Tâche de conversion LoRA démarrée." - self.BIN_FILES = "Fichiers binaires (*.bin)" self.BROWSING_FOR_LORA_INPUT_DIRECTORY = ( - "Recherche du répertoire d'entrée LoRA..." + "Parcourir pour le répertoire d'entrée LoRA..." + ) + self.BROWSING_FOR_LORA_OUTPUT_FILE = ( + "Parcourir pour le fichier de sortie LoRA..." ) - self.BROWSING_FOR_LORA_OUTPUT_FILE = "Recherche du fichier de sortie LoRA..." self.CONVERTING_LORA = "Conversion LoRA" self.LORA_CONVERSION_FINISHED = "Conversion LoRA terminée." - self.LORA_FILE_MOVED = "Fichier LoRA déplacé de {} à {}." + self.LORA_FILE_MOVED = "Fichier LoRA déplacé de {} vers {}." self.LORA_FILE_NOT_FOUND = "Fichier LoRA non trouvé : {}." self.ERROR_MOVING_LORA_FILE = "Erreur lors du déplacement du fichier LoRA : {}" - self.EXPORT_LORA = "Exporter LoRA" self.MODEL_PATH_REQUIRED = "Le chemin du modèle est requis." - self.OUTPUT_PATH_REQUIRED = "Le chemin de sortie est requis." self.AT_LEAST_ONE_LORA_ADAPTER_REQUIRED = ( "Au moins un adaptateur LoRA est requis." ) self.INVALID_LORA_SCALE_VALUE = "Valeur d'échelle LoRA invalide." self.ERROR_STARTING_LORA_EXPORT = ( - "Erreur lors du démarrage de l'exportation LoRA : {}" - ) - self.LORA_EXPORT_TASK_STARTED = "Tâche d'exportation LoRA démarrée." - self.GGML_LORA_ADAPTERS = "Adaptateurs LoRA GGML" - self.SELECT_LORA_ADAPTER_FILES = "Sélectionner les fichiers d'adaptateur LoRA" - self.ADD_ADAPTER = "Ajouter un adaptateur" - self.DELETE_ADAPTER = "Supprimer" - self.LORA_SCALE = "Échelle LoRA" - self.ENTER_LORA_SCALE_VALUE = "Entrez la valeur d'échelle LoRA (Optionnel)" - self.NUMBER_OF_THREADS_FOR_LORA_EXPORT = ( - "Nombre de threads pour l'exportation LoRA" + "Erreur lors du démarrage de l'export LoRA : {}" ) - self.EXPORTING_LORA = "Exportation de LoRA..." + self.LORA_EXPORT_TASK_STARTED = "Tâche d'export LoRA démarrée." + self.EXPORTING_LORA = "Export LoRA..." self.BROWSING_FOR_EXPORT_LORA_MODEL_FILE = ( - "Recherche du fichier de modèle LoRA à exporter..." + "Parcourir pour le fichier de modèle d'export LoRA..." ) self.BROWSING_FOR_EXPORT_LORA_OUTPUT_FILE = ( - "Recherche du fichier de sortie LoRA à exporter..." + "Parcourir pour le fichier de sortie d'export LoRA..." ) self.ADDING_LORA_ADAPTER = "Ajout d'un adaptateur LoRA..." self.DELETING_LORA_ADAPTER = "Suppression de l'adaptateur LoRA..." - self.LORA_FILES = "Fichiers LoRA (*.bin)" self.SELECT_LORA_ADAPTER_FILE = "Sélectionner le fichier d'adaptateur LoRA" - self.STARTING_LORA_EXPORT = "Démarrage de l'exportation LoRA..." - self.OUTPUT_TYPE = "Type de sortie" + self.STARTING_LORA_EXPORT = "Démarrage de l'export LoRA..." self.SELECT_OUTPUT_TYPE = "Sélectionner le type de sortie (GGUF ou GGML)" - self.GGUF_AND_BIN_FILES = "Fichiers GGUF et binaires (*.gguf *.bin)" self.BASE_MODEL = "Modèle de base" - self.SELECT_BASE_MODEL_FILE = "Sélectionner le fichier du modèle de base (GGUF)" + self.SELECT_BASE_MODEL_FILE = "Sélectionner le fichier de modèle de base (GGUF)" self.BASE_MODEL_PATH_REQUIRED = ( "Le chemin du modèle de base est requis pour la sortie GGUF." ) - self.BROWSING_FOR_BASE_MODEL_FILE = "Recherche du fichier du modèle de base..." + self.BROWSING_FOR_BASE_MODEL_FILE = ( + "Parcourir pour le fichier de modèle de base..." + ) self.SELECT_BASE_MODEL_FOLDER = ( - "Sélectionner le dossier du modèle de base (contenant safetensors)" + "Sélectionner le dossier du modèle de base (contenant les safetensors)" ) self.BROWSING_FOR_BASE_MODEL_FOLDER = ( - "Recherche du dossier du modèle de base..." + "Parcourir pour le dossier du modèle de base..." ) - self.LORA_CONVERSION_FROM_TO = "Conversion LoRA de {} à {}" - self.GENERATING_IMATRIX_FOR = "Génération d'IMatrix pour {}" + self.LORA_CONVERSION_FROM_TO = "Conversion LoRA de {} vers {}" + self.GENERATING_IMATRIX_FOR = "Génération de l'IMatrix pour {}" self.MODEL_PATH_REQUIRED_FOR_IMATRIX = ( - "Le chemin du modèle est requis pour la génération d'IMatrix." + "Le chemin du modèle est requis pour la génération IMatrix." + ) + self.NO_ASSET_SELECTED_FOR_CUDA_CHECK = ( + "Aucun asset sélectionné pour la vérification CUDA" + ) + self.NO_QUANTIZATION_TYPE_SELECTED = "Aucun type de quantification sélectionné. Veuillez sélectionner au moins un type de quantification." + self.STARTING_HF_TO_GGUF_CONVERSION = ( + "Démarrage de la conversion HuggingFace vers GGUF" + ) + self.MODEL_DIRECTORY_REQUIRED = "Le répertoire du modèle est requis" + self.HF_TO_GGUF_CONVERSION_COMMAND = "Commande de conversion HF vers GGUF : {}" + self.CONVERTING_TO_GGUF = "Conversion de {} vers GGUF" + self.ERROR_STARTING_HF_TO_GGUF_CONVERSION = ( + "Erreur lors du démarrage de la conversion HuggingFace vers GGUF : {}" + ) + self.HF_TO_GGUF_CONVERSION_TASK_STARTED = ( + "Tâche de conversion HuggingFace vers GGUF démarrée" ) class _SimplifiedChinese(_Localization): def __init__(self): super().__init__() + + # 通用界面 self.WINDOW_TITLE = "AutoGGUF（自动GGUF模型量化器）" self.RAM_USAGE = "内存使用率：" self.CPU_USAGE = "CPU使用率：" @@ -894,28 +705,43 @@ def __init__(self): self.LOGS_PATH = "日志路径：" self.BROWSE = "浏览" self.AVAILABLE_MODELS = "可用模型：" + self.REFRESH_MODELS = "刷新模型" + + # 量化 self.QUANTIZATION_TYPE = "量化类型：" self.ALLOW_REQUANTIZE = "允许重新量化" self.LEAVE_OUTPUT_TENSOR = "保留输出张量" self.PURE = "纯净" - self.IMATRIX = "IMatrix：" + self.IMATRIX = "重要性矩阵：" self.INCLUDE_WEIGHTS = "包含权重：" self.EXCLUDE_WEIGHTS = "排除权重：" self.USE_OUTPUT_TENSOR_TYPE = "使用输出张量类型" - self.USE_TOKEN_EMBEDDING_TYPE = "使用令牌嵌入类型" + self.USE_TOKEN_EMBEDDING_TYPE = "使用词元嵌入类型" self.KEEP_SPLIT = "保持分割" self.KV_OVERRIDES = "KV覆盖：" self.ADD_NEW_OVERRIDE = "添加新覆盖" self.QUANTIZE_MODEL = "量化模型" + self.EXTRA_ARGUMENTS = "额外参数：" + self.EXTRA_ARGUMENTS_LABEL = "附加命令行参数" + self.QUANTIZATION_COMMAND = "量化命令" + + # 预设 self.SAVE_PRESET = "保存预设" self.LOAD_PRESET = "加载预设" + + # 任务 self.TASKS = "任务：" + + # llama.cpp下载 self.DOWNLOAD_LLAMACPP = "下载llama.cpp" - self.SELECT_RELEASE = "选择发布版本：" + self.SELECT_RELEASE = "选择版本：" self.SELECT_ASSET = "选择资源：" self.EXTRACT_CUDA_FILES = "提取CUDA文件" self.SELECT_CUDA_BACKEND = "选择CUDA后端：" self.DOWNLOAD = "下载" + self.REFRESH_RELEASES = "刷新版本" + + # IMatrix生成 self.IMATRIX_GENERATION = "IMatrix生成" self.DATA_FILE = "数据文件：" self.MODEL = "模型：" @@ -924,6 +750,50 @@ def __init__(self): self.GPU_OFFLOAD = "GPU卸载：" self.AUTO = "自动" self.GENERATE_IMATRIX = "生成IMatrix" + self.CONTEXT_SIZE = "上下文大小：" + self.CONTEXT_SIZE_FOR_IMATRIX = "IMatrix生成的上下文大小" + self.THREADS = "线程数：" + self.NUMBER_OF_THREADS_FOR_IMATRIX = "IMatrix生成的线程数" + self.IMATRIX_GENERATION_COMMAND = "IMatrix生成命令" + + # LoRA转换 + self.LORA_CONVERSION = "LoRA转换" + self.LORA_INPUT_PATH = "LoRA输入路径" + self.LORA_OUTPUT_PATH = "LoRA输出路径" + self.SELECT_LORA_INPUT_DIRECTORY = "选择LoRA输入目录" + self.SELECT_LORA_OUTPUT_FILE = "选择LoRA输出文件" + self.CONVERT_LORA = "转换LoRA" + self.LORA_CONVERSION_COMMAND = "LoRA转换命令" + + # LoRA导出 + self.EXPORT_LORA = "导出LoRA" + self.GGML_LORA_ADAPTERS = "GGML LoRA适配器" + self.SELECT_LORA_ADAPTER_FILES = "选择LoRA适配器文件" + self.ADD_ADAPTER = "添加适配器" + self.DELETE_ADAPTER = "删除" + self.LORA_SCALE = "LoRA比例" + self.ENTER_LORA_SCALE_VALUE = "输入LoRA比例值（可选）" + self.NUMBER_OF_THREADS_FOR_LORA_EXPORT = "LoRA导出的线程数" + self.LORA_EXPORT_COMMAND = "LoRA导出命令" + + # HuggingFace到GGUF转换 + self.HF_TO_GGUF_CONVERSION = "HuggingFace到GGUF转换" + self.MODEL_DIRECTORY = "模型目录：" + self.OUTPUT_FILE = "输出文件：" + self.OUTPUT_TYPE = "输出类型：" + self.VOCAB_ONLY = "仅词汇表" + self.USE_TEMP_FILE = "使用临时文件" + self.NO_LAZY_EVALUATION = "不使用延迟评估" + self.MODEL_NAME = "模型名称：" + self.VERBOSE = "详细模式" + self.SPLIT_MAX_SIZE = "最大分割大小：" + self.DRY_RUN = "试运行" + self.CONVERT_HF_TO_GGUF = "转换HF到GGUF" + self.SELECT_HF_MODEL_DIRECTORY = "选择HuggingFace模型目录" + self.BROWSE_FOR_HF_MODEL_DIRECTORY = "浏览HuggingFace模型目录" + self.BROWSE_FOR_HF_TO_GGUF_OUTPUT = "浏览HuggingFace到GGUF输出文件" + + # 通用消息 self.ERROR = "错误" self.WARNING = "警告" self.PROPERTIES = "属性" @@ -931,9 +801,22 @@ def __init__(self): self.RESTART = "重启" self.DELETE = "删除" self.CONFIRM_DELETION = "您确定要删除此任务吗？" - self.TASK_RUNNING_WARNING = "某些任务仍在运行。您确定要退出吗？" + self.TASK_RUNNING_WARNING = "一些任务仍在运行。您确定要退出吗？" self.YES = "是" self.NO = "否" + self.COMPLETED = "已完成" + + # 文件类型 + self.ALL_FILES = "所有文件 (*)" + self.GGUF_FILES = "GGUF文件 (*.gguf)" + self.DAT_FILES = "DAT文件 (*.dat)" + self.JSON_FILES = "JSON文件 (*.json)" + self.BIN_FILES = "二进制文件 (*.bin)" + self.LORA_FILES = "LoRA文件 (*.bin)" + self.GGUF_AND_BIN_FILES = "GGUF和二进制文件 (*.gguf *.bin)" + self.SHARDED = "分片" + + # 状态消息 self.DOWNLOAD_COMPLETE = "下载完成" self.CUDA_EXTRACTION_FAILED = "CUDA提取失败" self.PRESET_SAVED = "预设已保存" @@ -942,20 +825,14 @@ def __init__(self): self.DOWNLOAD_FAILED = "下载失败" self.NO_BACKEND_SELECTED = "未选择后端" 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.IN_PROGRESS = "进行中" + self.LLAMACPP_DOWNLOADED_EXTRACTED = "llama.cpp二进制文件已下载并解压到{0}" 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}" + self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = "未找到合适的CUDA后端进行提取" + self.ERROR_FETCHING_RELEASES = "获取版本时出错：{0}" self.CONFIRM_DELETION_TITLE = "确认删除" self.LOG_FOR = "{0}的日志" - self.ALL_FILES = "所有文件 (*)" - self.GGUF_FILES = "GGUF文件 (*.gguf)" - self.DAT_FILES = "DAT文件 (*.dat)" - self.JSON_FILES = "JSON文件 (*.json)" self.FAILED_LOAD_PRESET = "加载预设失败：{0}" self.INITIALIZING_AUTOGGUF = "初始化AutoGGUF应用程序" self.AUTOGGUF_INITIALIZATION_COMPLETE = "AutoGGUF初始化完成" @@ -965,27 +842,24 @@ def __init__(self): self.SAVING_PRESET = "保存预设" self.PRESET_SAVED_TO = "预设已保存到{0}" self.LOADING_PRESET = "加载预设" - self.PRESET_LOADED_FROM = "从{0}加载了预设" + self.PRESET_LOADED_FROM = "预设已从{0}加载" self.ADDING_KV_OVERRIDE = "添加KV覆盖：{0}" self.SAVING_TASK_PRESET = "保存{0}的任务预设" self.TASK_PRESET_SAVED = "任务预设已保存" self.TASK_PRESET_SAVED_TO = "任务预设已保存到{0}" self.RESTARTING_TASK = "重启任务：{0}" - self.IN_PROGRESS = "进行中" - self.DOWNLOAD_FINISHED_EXTRACTED_TO = "下载完成。已提取到：{0}" - self.LLAMACPP_DOWNLOADED_AND_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.NO_SUITABLE_CUDA_BACKEND_FOUND = "未找到适合提取的CUDA后端" + self.DOWNLOAD_FINISHED_EXTRACTED_TO = "下载完成。已解压到：{0}" + self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "llama.cpp二进制文件已下载并解压到{0}" + self.NO_SUITABLE_CUDA_BACKEND_FOUND = "未找到合适的CUDA后端进行提取" self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = ( - "llama.cpp二进制文件已下载并提取到{0}" + "llama.cpp二进制文件已下载并解压到{0}" ) - self.REFRESHING_LLAMACPP_RELEASES = "刷新llama.cpp发布版本" + self.REFRESHING_LLAMACPP_RELEASES = "刷新llama.cpp版本" self.UPDATING_ASSET_LIST = "更新资源列表" self.UPDATING_CUDA_OPTIONS = "更新CUDA选项" self.STARTING_LLAMACPP_DOWNLOAD = "开始下载llama.cpp" self.UPDATING_CUDA_BACKENDS = "更新CUDA后端" - self.NO_CUDA_BACKEND_SELECTED = "未选择要提取的CUDA后端" + self.NO_CUDA_BACKEND_SELECTED = "未选择CUDA后端进行提取" self.EXTRACTING_CUDA_FILES = "从{0}提取CUDA文件到{1}" self.DOWNLOAD_ERROR = "下载错误：{0}" self.SHOWING_TASK_CONTEXT_MENU = "显示任务上下文菜单" @@ -1003,7 +877,7 @@ def __init__(self): self.SELECT_LOGS_DIRECTORY = "选择日志目录" self.BROWSING_FOR_IMATRIX_FILE = "浏览IMatrix文件" self.SELECT_IMATRIX_FILE = "选择IMatrix文件" - self.RAM_USAGE_FORMAT = "{0:.1f}%（{1} MB / {2} MB）" + self.RAM_USAGE_FORMAT = "{0:.1f}% ({1} MB / {2} MB)" self.CPU_USAGE_FORMAT = "CPU使用率：{0:.1f}%" self.VALIDATING_QUANTIZATION_INPUTS = "验证量化输入" self.MODELS_PATH_REQUIRED = "需要模型路径" @@ -1012,7 +886,7 @@ def __init__(self): self.STARTING_MODEL_QUANTIZATION = "开始模型量化" self.INPUT_FILE_NOT_EXIST = "输入文件'{0}'不存在。" self.QUANTIZING_MODEL_TO = "将{0}量化为{1}" - self.QUANTIZATION_TASK_STARTED = "已启动{0}的量化任务" + self.QUANTIZATION_TASK_STARTED = "已开始{0}的量化任务" self.ERROR_STARTING_QUANTIZATION = "启动量化时出错：{0}" self.UPDATING_MODEL_INFO = "更新模型信息：{0}" self.TASK_FINISHED = "任务完成：{0}" @@ -1027,24 +901,24 @@ def __init__(self): self.BACKEND_PATH_NOT_EXIST = "后端路径不存在：{0}" self.GENERATING_IMATRIX = "生成IMatrix" self.ERROR_STARTING_IMATRIX_GENERATION = "启动IMatrix生成时出错：{0}" - self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix生成任务已启动" + self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix生成任务已开始" self.ERROR_MESSAGE = "错误：{0}" self.TASK_ERROR = "任务错误：{0}" self.APPLICATION_CLOSING = "应用程序正在关闭" self.APPLICATION_CLOSED = "应用程序已关闭" self.SELECT_QUANTIZATION_TYPE = "选择量化类型" - self.ALLOWS_REQUANTIZING = "允许重新量化已经量化的张量" - self.LEAVE_OUTPUT_WEIGHT = "将保留output.weight不被（重新）量化" + self.ALLOWS_REQUANTIZING = "允许重新量化已量化的张量" + self.LEAVE_OUTPUT_WEIGHT = "将保持output.weight不被（重新）量化" self.DISABLE_K_QUANT_MIXTURES = "禁用k-quant混合并将所有张量量化为相同类型" self.USE_DATA_AS_IMPORTANCE_MATRIX = "使用文件中的数据作为量化优化的重要性矩阵" self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "对这些张量使用重要性矩阵" self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = "不对这些张量使用重要性矩阵" self.OUTPUT_TENSOR_TYPE = "输出张量类型：" 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.TOKEN_EMBEDDING_TYPE = "词元嵌入类型：" + self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = "对词元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" + "将生成与输入相同分片的量化模型" ) 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" @@ -1052,146 +926,208 @@ def __init__(self): self.OUTPUT_PATH_FOR_GENERATED_IMATRIX = "生成的IMatrix的输出路径" self.HOW_OFTEN_TO_SAVE_IMATRIX = "保存IMatrix的频率" self.SET_GPU_OFFLOAD_VALUE = "设置GPU卸载值（-ngl）" - self.COMPLETED = "已完成" - self.REFRESH_MODELS = "刷新模型" - self.REFRESH_MODELS = "刷新模型" - self.EXTRA_ARGUMENTS = "额外参数：" - self.EXTRA_ARGUMENTS_LABEL = "附加命令行参数" - self.CONTEXT_SIZE = "上下文大小：" - self.CONTEXT_SIZE_FOR_IMATRIX = "IMatrix生成的上下文大小" - self.THREADS = "线程数：" - self.NUMBER_OF_THREADS_FOR_IMATRIX = "IMatrix生成的线程数" - self.LORA_CONVERSION = "LoRA转换" - self.LORA_INPUT_PATH = "LoRA输入路径" - self.LORA_OUTPUT_PATH = "LoRA输出路径" - self.SELECT_LORA_INPUT_DIRECTORY = "选择LoRA输入目录" - self.SELECT_LORA_OUTPUT_FILE = "选择LoRA输出文件" - self.CONVERT_LORA = "转换LoRA" self.STARTING_LORA_CONVERSION = "开始LoRA转换" self.LORA_INPUT_PATH_REQUIRED = "需要LoRA输入路径。" self.LORA_OUTPUT_PATH_REQUIRED = "需要LoRA输出路径。" self.ERROR_STARTING_LORA_CONVERSION = "启动LoRA转换时出错：{}" - self.LORA_CONVERSION_TASK_STARTED = "LoRA转换任务已启动。" - self.BIN_FILES = "二进制文件 (*.bin)" - self.BROWSING_FOR_LORA_INPUT_DIRECTORY = "正在浏览LoRA输入目录..." - self.BROWSING_FOR_LORA_OUTPUT_FILE = "正在浏览LoRA输出文件..." + self.LORA_CONVERSION_TASK_STARTED = "LoRA转换任务已开始。" + self.BROWSING_FOR_LORA_INPUT_DIRECTORY = "浏览LoRA输入目录..." + self.BROWSING_FOR_LORA_OUTPUT_FILE = "浏览LoRA输出文件..." self.CONVERTING_LORA = "LoRA转换" self.LORA_CONVERSION_FINISHED = "LoRA转换完成。" self.LORA_FILE_MOVED = "LoRA文件已从{}移动到{}。" self.LORA_FILE_NOT_FOUND = "未找到LoRA文件：{}。" self.ERROR_MOVING_LORA_FILE = "移动LoRA文件时出错：{}" - self.EXPORT_LORA = "导出LoRA" self.MODEL_PATH_REQUIRED = "需要模型路径。" - self.OUTPUT_PATH_REQUIRED = "需要输出路径。" self.AT_LEAST_ONE_LORA_ADAPTER_REQUIRED = "至少需要一个LoRA适配器。" self.INVALID_LORA_SCALE_VALUE = "无效的LoRA比例值。" self.ERROR_STARTING_LORA_EXPORT = "启动LoRA导出时出错：{}" - self.LORA_EXPORT_TASK_STARTED = "LoRA导出任务已启动。" - self.GGML_LORA_ADAPTERS = "GGML LoRA适配器" - self.SELECT_LORA_ADAPTER_FILES = "选择LoRA适配器文件" - self.ADD_ADAPTER = "添加适配器" - self.DELETE_ADAPTER = "删除" - self.LORA_SCALE = "LoRA比例" - self.ENTER_LORA_SCALE_VALUE = "输入LoRA比例值（可选）" - self.NUMBER_OF_THREADS_FOR_LORA_EXPORT = "LoRA导出的线程数" - self.EXPORTING_LORA = "正在导出LoRA..." - self.BROWSING_FOR_EXPORT_LORA_MODEL_FILE = "正在浏览导出LoRA模型文件..." - self.BROWSING_FOR_EXPORT_LORA_OUTPUT_FILE = "正在浏览导出LoRA输出文件..." - self.ADDING_LORA_ADAPTER = "正在添加LoRA适配器..." - self.DELETING_LORA_ADAPTER = "正在删除LoRA适配器..." - self.LORA_FILES = "LoRA文件 (*.bin)" + self.LORA_EXPORT_TASK_STARTED = "LoRA导出任务已开始。" + self.EXPORTING_LORA = "导出LoRA..." + self.BROWSING_FOR_EXPORT_LORA_MODEL_FILE = "浏览导出LoRA模型文件..." + self.BROWSING_FOR_EXPORT_LORA_OUTPUT_FILE = "浏览导出LoRA输出文件..." + self.ADDING_LORA_ADAPTER = "添加LoRA适配器..." + self.DELETING_LORA_ADAPTER = "删除LoRA适配器..." self.SELECT_LORA_ADAPTER_FILE = "选择LoRA适配器文件" self.STARTING_LORA_EXPORT = "开始LoRA导出..." - self.OUTPUT_TYPE = "输出类型" self.SELECT_OUTPUT_TYPE = "选择输出类型（GGUF或GGML）" - self.GGUF_AND_BIN_FILES = "GGUF和二进制文件 (*.gguf *.bin)" self.BASE_MODEL = "基础模型" self.SELECT_BASE_MODEL_FILE = "选择基础模型文件（GGUF）" self.BASE_MODEL_PATH_REQUIRED = "GGUF输出需要基础模型路径。" - self.BROWSING_FOR_BASE_MODEL_FILE = "正在浏览基础模型文件..." + self.BROWSING_FOR_BASE_MODEL_FILE = "浏览基础模型文件..." self.SELECT_BASE_MODEL_FOLDER = "选择基础模型文件夹（包含safetensors）" - self.BROWSING_FOR_BASE_MODEL_FOLDER = "正在浏览基础模型文件夹..." - self.LORA_CONVERSION_FROM_TO = "LoRA从{}转换到{}" - self.GENERATING_IMATRIX_FOR = "正在为{}生成IMatrix" + self.BROWSING_FOR_BASE_MODEL_FOLDER = "浏览基础模型文件夹..." + self.LORA_CONVERSION_FROM_TO = "LoRA转换从{}到{}" + self.GENERATING_IMATRIX_FOR = "为{}生成IMatrix" self.MODEL_PATH_REQUIRED_FOR_IMATRIX = "IMatrix生成需要模型路径。" + self.NO_ASSET_SELECTED_FOR_CUDA_CHECK = "未选择用于CUDA检查的资源" + self.NO_QUANTIZATION_TYPE_SELECTED = "未选择量化类型。请至少选择一种量化类型。" + self.STARTING_HF_TO_GGUF_CONVERSION = "开始HuggingFace到GGUF转换" + self.MODEL_DIRECTORY_REQUIRED = "需要模型目录" + self.HF_TO_GGUF_CONVERSION_COMMAND = "HF到GGUF转换命令：{}" + self.CONVERTING_TO_GGUF = "将{}转换为GGUF" + self.ERROR_STARTING_HF_TO_GGUF_CONVERSION = ( + "启动HuggingFace到GGUF转换时出错：{}" + ) + self.HF_TO_GGUF_CONVERSION_TASK_STARTED = "HuggingFace到GGUF转换任务已开始" class _Spanish(_Localization): def __init__(self): super().__init__() - self.WINDOW_TITLE = "AutoGGUF (cuantizador automático de modelos GGUF)" + + # Interfaz de usuario general + self.WINDOW_TITLE = "AutoGGUF (cuantificador automático de modelos GGUF)" self.RAM_USAGE = "Uso de RAM:" self.CPU_USAGE = "Uso de CPU:" self.BACKEND = "Backend de Llama.cpp:" - self.REFRESH_BACKENDS = "Actualizar Backends" - self.MODELS_PATH = "Ruta de Modelos:" - self.OUTPUT_PATH = "Ruta de Salida:" - self.LOGS_PATH = "Ruta de Registros:" + self.REFRESH_BACKENDS = "Actualizar backends" + self.MODELS_PATH = "Ruta de modelos:" + self.OUTPUT_PATH = "Ruta de salida:" + self.LOGS_PATH = "Ruta de registros:" self.BROWSE = "Explorar" - self.AVAILABLE_MODELS = "Modelos Disponibles:" - self.QUANTIZATION_TYPE = "Tipo de Cuantización:" - self.ALLOW_REQUANTIZE = "Permitir Recuantización" - self.LEAVE_OUTPUT_TENSOR = "Dejar Tensor de Salida" + self.AVAILABLE_MODELS = "Modelos disponibles:" + self.REFRESH_MODELS = "Actualizar modelos" + + # Cuantificación + self.QUANTIZATION_TYPE = "Tipo de cuantificación:" + self.ALLOW_REQUANTIZE = "Permitir recuantificación" + self.LEAVE_OUTPUT_TENSOR = "Dejar tensor de salida" self.PURE = "Puro" self.IMATRIX = "IMatrix:" - self.INCLUDE_WEIGHTS = "Incluir Pesos:" - self.EXCLUDE_WEIGHTS = "Excluir Pesos:" - self.USE_OUTPUT_TENSOR_TYPE = "Usar Tipo de Tensor de Salida" - self.USE_TOKEN_EMBEDDING_TYPE = "Usar Tipo de Incrustación de Token" - self.KEEP_SPLIT = "Mantener División" + self.INCLUDE_WEIGHTS = "Incluir pesos:" + self.EXCLUDE_WEIGHTS = "Excluir pesos:" + self.USE_OUTPUT_TENSOR_TYPE = "Usar tipo de tensor de salida" + self.USE_TOKEN_EMBEDDING_TYPE = "Usar tipo de embedding de token" + self.KEEP_SPLIT = "Mantener división" self.KV_OVERRIDES = "Anulaciones KV:" - self.ADD_NEW_OVERRIDE = "Agregar nueva anulación" - self.QUANTIZE_MODEL = "Cuantizar Modelo" - self.SAVE_PRESET = "Guardar Preajuste" - self.LOAD_PRESET = "Cargar Preajuste" + self.ADD_NEW_OVERRIDE = "Añadir nueva anulación" + self.QUANTIZE_MODEL = "Cuantificar modelo" + self.EXTRA_ARGUMENTS = "Argumentos extra:" + self.EXTRA_ARGUMENTS_LABEL = "Argumentos adicionales de línea de comandos" + self.QUANTIZATION_COMMAND = "Comando de cuantificación" + + # Preajustes + self.SAVE_PRESET = "Guardar preajuste" + self.LOAD_PRESET = "Cargar preajuste" + + # Tareas self.TASKS = "Tareas:" + + # Descarga de llama.cpp self.DOWNLOAD_LLAMACPP = "Descargar llama.cpp" - self.SELECT_RELEASE = "Seleccionar Versión:" - self.SELECT_ASSET = "Seleccionar Activo:" + self.SELECT_RELEASE = "Seleccionar versión:" + self.SELECT_ASSET = "Seleccionar asset:" self.EXTRACT_CUDA_FILES = "Extraer archivos CUDA" - self.SELECT_CUDA_BACKEND = "Seleccionar Backend CUDA:" + self.SELECT_CUDA_BACKEND = "Seleccionar backend CUDA:" self.DOWNLOAD = "Descargar" + self.REFRESH_RELEASES = "Actualizar versiones" + + # Generación de IMatrix self.IMATRIX_GENERATION = "Generación de IMatrix" - self.DATA_FILE = "Archivo de Datos:" + self.DATA_FILE = "Archivo de datos:" self.MODEL = "Modelo:" self.OUTPUT = "Salida:" - self.OUTPUT_FREQUENCY = "Frecuencia de Salida:" - self.GPU_OFFLOAD = "Descarga GPU:" + self.OUTPUT_FREQUENCY = "Frecuencia de salida:" + self.GPU_OFFLOAD = "Descarga en GPU:" self.AUTO = "Auto" self.GENERATE_IMATRIX = "Generar IMatrix" + self.CONTEXT_SIZE = "Tamaño de contexto:" + self.CONTEXT_SIZE_FOR_IMATRIX = "Tamaño de contexto para generación de IMatrix" + self.THREADS = "Hilos:" + self.NUMBER_OF_THREADS_FOR_IMATRIX = ( + "Número de hilos para generación de IMatrix" + ) + self.IMATRIX_GENERATION_COMMAND = "Comando de generación de IMatrix" + + # Conversión LoRA + self.LORA_CONVERSION = "Conversión LoRA" + self.LORA_INPUT_PATH = "Ruta de entrada LoRA" + self.LORA_OUTPUT_PATH = "Ruta de salida LoRA" + self.SELECT_LORA_INPUT_DIRECTORY = "Seleccionar directorio de entrada LoRA" + self.SELECT_LORA_OUTPUT_FILE = "Seleccionar archivo de salida LoRA" + self.CONVERT_LORA = "Convertir LoRA" + self.LORA_CONVERSION_COMMAND = "Comando de conversión LoRA" + + # Exportación LoRA + self.EXPORT_LORA = "Exportar LoRA" + self.GGML_LORA_ADAPTERS = "Adaptadores LoRA GGML" + self.SELECT_LORA_ADAPTER_FILES = "Seleccionar archivos de adaptador LoRA" + self.ADD_ADAPTER = "Añadir adaptador" + self.DELETE_ADAPTER = "Eliminar" + self.LORA_SCALE = "Escala LoRA" + self.ENTER_LORA_SCALE_VALUE = "Introducir valor de escala LoRA (Opcional)" + self.NUMBER_OF_THREADS_FOR_LORA_EXPORT = "Número de hilos para exportación LoRA" + self.LORA_EXPORT_COMMAND = "Comando de exportación LoRA" + + # Conversión de HuggingFace a GGUF + self.HF_TO_GGUF_CONVERSION = "Conversión de HuggingFace a GGUF" + self.MODEL_DIRECTORY = "Directorio del modelo:" + self.OUTPUT_FILE = "Archivo de salida:" + self.OUTPUT_TYPE = "Tipo de salida:" + self.VOCAB_ONLY = "Solo vocabulario" + self.USE_TEMP_FILE = "Usar archivo temporal" + self.NO_LAZY_EVALUATION = "Sin evaluación perezosa" + self.MODEL_NAME = "Nombre del modelo:" + self.VERBOSE = "Detallado" + self.SPLIT_MAX_SIZE = "Tamaño máximo de división:" + self.DRY_RUN = "Simulación" + self.CONVERT_HF_TO_GGUF = "Convertir HF a GGUF" + self.SELECT_HF_MODEL_DIRECTORY = "Seleccionar directorio del modelo HuggingFace" + self.BROWSE_FOR_HF_MODEL_DIRECTORY = ( + "Explorar directorio del modelo HuggingFace" + ) + self.BROWSE_FOR_HF_TO_GGUF_OUTPUT = ( + "Explorar archivo de salida HuggingFace a GGUF" + ) + + # Mensajes generales self.ERROR = "Error" self.WARNING = "Advertencia" self.PROPERTIES = "Propiedades" self.CANCEL = "Cancelar" self.RESTART = "Reiniciar" self.DELETE = "Eliminar" - self.CONFIRM_DELETION = "¿Estás seguro de que quieres eliminar esta tarea?" - self.TASK_RUNNING_WARNING = "Algunas tareas aún se están ejecutando. ¿Estás seguro de que quieres salir?" + self.CONFIRM_DELETION = "¿Está seguro de que desea eliminar esta tarea?" + self.TASK_RUNNING_WARNING = ( + "Algunas tareas aún están en ejecución. ¿Está seguro de que desea salir?" + ) self.YES = "Sí" self.NO = "No" - self.DOWNLOAD_COMPLETE = "Descarga Completa" - self.CUDA_EXTRACTION_FAILED = "Extracción de CUDA Fallida" - self.PRESET_SAVED = "Preajuste Guardado" - self.PRESET_LOADED = "Preajuste Cargado" - self.NO_ASSET_SELECTED = "Ningún activo seleccionado" - self.DOWNLOAD_FAILED = "Descarga fallida" - self.NO_BACKEND_SELECTED = "Ningún backend seleccionado" - self.NO_MODEL_SELECTED = "Ningún modelo seleccionado" - self.REFRESH_RELEASES = "Actualizar Versiones" + self.COMPLETED = "Completado" + + # Tipos de archivo + self.ALL_FILES = "Todos los archivos (*)" + self.GGUF_FILES = "Archivos GGUF (*.gguf)" + self.DAT_FILES = "Archivos DAT (*.dat)" + self.JSON_FILES = "Archivos JSON (*.json)" + self.BIN_FILES = "Archivos binarios (*.bin)" + self.LORA_FILES = "Archivos LoRA (*.bin)" + self.GGUF_AND_BIN_FILES = "Archivos GGUF y binarios (*.gguf *.bin)" + self.SHARDED = "fragmentado" + + # Mensajes de estado + self.DOWNLOAD_COMPLETE = "Descarga completa" + self.CUDA_EXTRACTION_FAILED = "Falló la extracción de CUDA" + self.PRESET_SAVED = "Preajuste guardado" + self.PRESET_LOADED = "Preajuste cargado" + self.NO_ASSET_SELECTED = "No se seleccionó ningún asset" + self.DOWNLOAD_FAILED = "Falló la descarga" + self.NO_BACKEND_SELECTED = "No se seleccionó ningún backend" + self.NO_MODEL_SELECTED = "No se seleccionó ningún modelo" self.NO_SUITABLE_CUDA_BACKENDS = "No se encontraron backends CUDA adecuados" - self.LLAMACPP_DOWNLOADED_EXTRACTED = "Binario de llama.cpp descargado y extraído en {0}\nArchivos CUDA extraídos en {1}" + self.IN_PROGRESS = "En progreso" + self.LLAMACPP_DOWNLOADED_EXTRACTED = ( + "Binario de llama.cpp descargado y extraído en {0}" + ) self.CUDA_FILES_EXTRACTED = "Archivos CUDA extraídos en" self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = ( "No se encontró un backend CUDA adecuado para la extracción" ) self.ERROR_FETCHING_RELEASES = "Error al obtener versiones: {0}" - self.CONFIRM_DELETION_TITLE = "Confirmar Eliminación" + self.CONFIRM_DELETION_TITLE = "Confirmar eliminación" self.LOG_FOR = "Registro para {0}" - self.ALL_FILES = "Todos los Archivos (*)" - self.GGUF_FILES = "Archivos GGUF (*.gguf)" - self.DAT_FILES = "Archivos DAT (*.dat)" - self.JSON_FILES = "Archivos JSON (*.json)" - self.FAILED_LOAD_PRESET = "Error al cargar el preajuste: {0}" + self.FAILED_LOAD_PRESET = "Falló la carga del preajuste: {0}" self.INITIALIZING_AUTOGGUF = "Inicializando aplicación AutoGGUF" self.AUTOGGUF_INITIALIZATION_COMPLETE = "Inicialización de AutoGGUF completa" self.REFRESHING_BACKENDS = "Actualizando backends" @@ -1201,14 +1137,15 @@ def __init__(self): self.PRESET_SAVED_TO = "Preajuste guardado en {0}" self.LOADING_PRESET = "Cargando preajuste" self.PRESET_LOADED_FROM = "Preajuste cargado desde {0}" - self.ADDING_KV_OVERRIDE = "Agregando anulación KV: {0}" + self.ADDING_KV_OVERRIDE = "Añadiendo anulación KV: {0}" self.SAVING_TASK_PRESET = "Guardando preajuste de tarea para {0}" - self.TASK_PRESET_SAVED = "Preajuste de Tarea Guardado" + self.TASK_PRESET_SAVED = "Preajuste de tarea guardado" self.TASK_PRESET_SAVED_TO = "Preajuste de tarea guardado en {0}" self.RESTARTING_TASK = "Reiniciando tarea: {0}" - self.IN_PROGRESS = "En Progreso" self.DOWNLOAD_FINISHED_EXTRACTED_TO = "Descarga finalizada. Extraído en: {0}" - self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "Binario de llama.cpp descargado y extraído en {0}\nArchivos CUDA extraídos en {1}" + self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = ( + "Binario de llama.cpp descargado y extraído en {0}" + ) self.NO_SUITABLE_CUDA_BACKEND_FOUND = ( "No se encontró un backend CUDA adecuado para la extracción" ) @@ -1216,11 +1153,13 @@ def __init__(self): "Binario de llama.cpp descargado y extraído en {0}" ) self.REFRESHING_LLAMACPP_RELEASES = "Actualizando versiones de llama.cpp" - self.UPDATING_ASSET_LIST = "Actualizando lista de activos" + self.UPDATING_ASSET_LIST = "Actualizando lista de assets" self.UPDATING_CUDA_OPTIONS = "Actualizando opciones de CUDA" self.STARTING_LLAMACPP_DOWNLOAD = "Iniciando descarga de llama.cpp" self.UPDATING_CUDA_BACKENDS = "Actualizando backends CUDA" - self.NO_CUDA_BACKEND_SELECTED = "No se seleccionó backend CUDA para extracción" + self.NO_CUDA_BACKEND_SELECTED = ( + "No se seleccionó ningún backend CUDA para la extracción" + ) self.EXTRACTING_CUDA_FILES = "Extrayendo archivos CUDA de {0} a {1}" self.DOWNLOAD_ERROR = "Error de descarga: {0}" self.SHOWING_TASK_CONTEXT_MENU = "Mostrando menú contextual de tarea" @@ -1229,35 +1168,35 @@ def __init__(self): self.CANCELED = "Cancelado" self.DELETING_TASK = "Eliminando tarea: {0}" self.LOADING_MODELS = "Cargando modelos" - self.LOADED_MODELS = "Cargados {0} modelos" + self.LOADED_MODELS = "Se cargaron {0} modelos" self.BROWSING_FOR_MODELS_DIRECTORY = "Explorando directorio de modelos" - self.SELECT_MODELS_DIRECTORY = "Seleccionar Directorio de Modelos" + self.SELECT_MODELS_DIRECTORY = "Seleccionar directorio de modelos" self.BROWSING_FOR_OUTPUT_DIRECTORY = "Explorando directorio de salida" - self.SELECT_OUTPUT_DIRECTORY = "Seleccionar Directorio de Salida" + self.SELECT_OUTPUT_DIRECTORY = "Seleccionar directorio de salida" self.BROWSING_FOR_LOGS_DIRECTORY = "Explorando directorio de registros" - self.SELECT_LOGS_DIRECTORY = "Seleccionar Directorio de Registros" + self.SELECT_LOGS_DIRECTORY = "Seleccionar directorio de registros" self.BROWSING_FOR_IMATRIX_FILE = "Explorando archivo IMatrix" - self.SELECT_IMATRIX_FILE = "Seleccionar Archivo IMatrix" + self.SELECT_IMATRIX_FILE = "Seleccionar archivo IMatrix" self.RAM_USAGE_FORMAT = "{0:.1f}% ({1} MB / {2} MB)" self.CPU_USAGE_FORMAT = "Uso de CPU: {0:.1f}%" - self.VALIDATING_QUANTIZATION_INPUTS = "Validando entradas de cuantización" + self.VALIDATING_QUANTIZATION_INPUTS = "Validando entradas de cuantificación" self.MODELS_PATH_REQUIRED = "Se requiere la ruta de modelos" self.OUTPUT_PATH_REQUIRED = "Se requiere la ruta de salida" self.LOGS_PATH_REQUIRED = "Se requiere la ruta de registros" - self.STARTING_MODEL_QUANTIZATION = "Iniciando cuantización de modelo" + self.STARTING_MODEL_QUANTIZATION = "Iniciando cuantificación del modelo" self.INPUT_FILE_NOT_EXIST = "El archivo de entrada '{0}' no existe." - self.QUANTIZING_MODEL_TO = "Cuantizando {0} a {1}" - self.QUANTIZATION_TASK_STARTED = "Tarea de cuantización iniciada para {0}" - self.ERROR_STARTING_QUANTIZATION = "Error al iniciar la cuantización: {0}" + self.QUANTIZING_MODEL_TO = "Cuantificando {0} a {1}" + self.QUANTIZATION_TASK_STARTED = "Tarea de cuantificación iniciada para {0}" + self.ERROR_STARTING_QUANTIZATION = "Error al iniciar la cuantificación: {0}" self.UPDATING_MODEL_INFO = "Actualizando información del modelo: {0}" self.TASK_FINISHED = "Tarea finalizada: {0}" self.SHOWING_TASK_DETAILS_FOR = "Mostrando detalles de la tarea para: {0}" self.BROWSING_FOR_IMATRIX_DATA_FILE = "Explorando archivo de datos IMatrix" - self.SELECT_DATA_FILE = "Seleccionar Archivo de Datos" + self.SELECT_DATA_FILE = "Seleccionar archivo de datos" self.BROWSING_FOR_IMATRIX_MODEL_FILE = "Explorando archivo de modelo IMatrix" - self.SELECT_MODEL_FILE = "Seleccionar Archivo de Modelo" + self.SELECT_MODEL_FILE = "Seleccionar archivo de modelo" self.BROWSING_FOR_IMATRIX_OUTPUT_FILE = "Explorando archivo de salida IMatrix" - self.SELECT_OUTPUT_FILE = "Seleccionar Archivo de Salida" + self.SELECT_OUTPUT_FILE = "Seleccionar archivo de salida" self.STARTING_IMATRIX_GENERATION = "Iniciando generación de IMatrix" self.BACKEND_PATH_NOT_EXIST = "La ruta del backend no existe: {0}" self.GENERATING_IMATRIX = "Generando IMatrix" @@ -1269,120 +1208,104 @@ def __init__(self): self.TASK_ERROR = "Error de tarea: {0}" self.APPLICATION_CLOSING = "Cerrando aplicación" self.APPLICATION_CLOSED = "Aplicación cerrada" - self.SELECT_QUANTIZATION_TYPE = "Seleccione el tipo de cuantización" + self.SELECT_QUANTIZATION_TYPE = "Seleccione el tipo de cuantificación" self.ALLOWS_REQUANTIZING = ( - "Permite recuantizar tensores que ya han sido cuantizados" + "Permite recuantificar tensores que ya han sido cuantificados" ) - self.LEAVE_OUTPUT_WEIGHT = "Dejará output.weight sin (re)cuantizar" - self.DISABLE_K_QUANT_MIXTURES = ( - "Desactiva las mezclas k-quant y cuantiza todos los tensores al mismo tipo" - ) - self.USE_DATA_AS_IMPORTANCE_MATRIX = "Usa los datos en el archivo como matriz de importancia para optimizaciones de cuantización" + self.LEAVE_OUTPUT_WEIGHT = "Dejará output.weight sin (re)cuantificar" + self.DISABLE_K_QUANT_MIXTURES = "Deshabilita las mezclas k-quant y cuantifica todos los tensores al mismo tipo" + self.USE_DATA_AS_IMPORTANCE_MATRIX = "Usa los datos en el archivo como matriz de importancia para optimizaciones de cuantificación" self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = ( - "Usar matriz de importancia para estos tensores" + "Usa matriz de importancia para estos tensores" ) self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = ( - "No usar matriz de importancia para estos tensores" + "No usa matriz de importancia para estos tensores" ) - self.OUTPUT_TENSOR_TYPE = "Tipo de Tensor de Salida:" + self.OUTPUT_TENSOR_TYPE = "Tipo de tensor de salida:" self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = ( - "Usar este tipo para el tensor output.weight" + "Usa este tipo para el tensor output.weight" ) - self.TOKEN_EMBEDDING_TYPE = "Tipo de Incrustación de Token:" + self.TOKEN_EMBEDDING_TYPE = "Tipo de embedding de token:" self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = ( - "Usar este tipo para el tensor de incrustaciones de token" + "Usa este tipo para el tensor de embeddings de token" ) self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = ( - "Generará el modelo cuantizado en los mismos fragmentos que la entrada" + "Generará el modelo cuantificado en los mismos fragmentos que la entrada" ) self.OVERRIDE_MODEL_METADATA = "Anular metadatos del modelo" self.INPUT_DATA_FILE_FOR_IMATRIX = ( "Archivo de datos de entrada para generación de IMatrix" ) - self.MODEL_TO_BE_QUANTIZED = "Modelo a cuantizar" + self.MODEL_TO_BE_QUANTIZED = "Modelo a cuantificar" self.OUTPUT_PATH_FOR_GENERATED_IMATRIX = ( "Ruta de salida para el IMatrix generado" ) self.HOW_OFTEN_TO_SAVE_IMATRIX = "Con qué frecuencia guardar el IMatrix" self.SET_GPU_OFFLOAD_VALUE = "Establecer valor de descarga GPU (-ngl)" - self.COMPLETED = "Completado" - self.REFRESH_MODELS = "Actualizar modelos" - self.REFRESH_MODELS = "Actualizar modelos" - self.EXTRA_ARGUMENTS = "Argumentos adicionales:" - self.EXTRA_ARGUMENTS_LABEL = "Argumentos adicionales de línea de comandos" - self.CONTEXT_SIZE = "Tamaño del contexto:" - self.CONTEXT_SIZE_FOR_IMATRIX = "Tamaño del contexto para generación de IMatrix" - self.THREADS = "Hilos:" - self.NUMBER_OF_THREADS_FOR_IMATRIX = ( - "Número de hilos para generación de IMatrix" - ) - self.LORA_CONVERSION = "Conversión LoRA" - self.LORA_INPUT_PATH = "Ruta de entrada LoRA" - self.LORA_OUTPUT_PATH = "Ruta de salida LoRA" - self.SELECT_LORA_INPUT_DIRECTORY = "Seleccionar directorio de entrada LoRA" - self.SELECT_LORA_OUTPUT_FILE = "Seleccionar archivo de salida LoRA" - self.CONVERT_LORA = "Convertir LoRA" self.STARTING_LORA_CONVERSION = "Iniciando conversión LoRA" self.LORA_INPUT_PATH_REQUIRED = "Se requiere la ruta de entrada LoRA." self.LORA_OUTPUT_PATH_REQUIRED = "Se requiere la ruta de salida LoRA." self.ERROR_STARTING_LORA_CONVERSION = "Error al iniciar la conversión LoRA: {}" self.LORA_CONVERSION_TASK_STARTED = "Tarea de conversión LoRA iniciada." - self.BIN_FILES = "Archivos binarios (*.bin)" self.BROWSING_FOR_LORA_INPUT_DIRECTORY = ( - "Buscando directorio de entrada LoRA..." + "Explorando directorio de entrada LoRA..." ) - self.BROWSING_FOR_LORA_OUTPUT_FILE = "Buscando archivo de salida LoRA..." - self.CONVERTING_LORA = "Convirtiendo LoRA" + self.BROWSING_FOR_LORA_OUTPUT_FILE = "Explorando archivo de salida LoRA..." + self.CONVERTING_LORA = "Conversión LoRA" self.LORA_CONVERSION_FINISHED = "Conversión LoRA finalizada." self.LORA_FILE_MOVED = "Archivo LoRA movido de {} a {}." self.LORA_FILE_NOT_FOUND = "Archivo LoRA no encontrado: {}." self.ERROR_MOVING_LORA_FILE = "Error al mover el archivo LoRA: {}" - self.EXPORT_LORA = "Exportar LoRA" self.MODEL_PATH_REQUIRED = "Se requiere la ruta del modelo." - self.OUTPUT_PATH_REQUIRED = "Se requiere la ruta de salida." self.AT_LEAST_ONE_LORA_ADAPTER_REQUIRED = ( "Se requiere al menos un adaptador LoRA." ) self.INVALID_LORA_SCALE_VALUE = "Valor de escala LoRA inválido." self.ERROR_STARTING_LORA_EXPORT = "Error al iniciar la exportación LoRA: {}" self.LORA_EXPORT_TASK_STARTED = "Tarea de exportación LoRA iniciada." - self.GGML_LORA_ADAPTERS = "Adaptadores LoRA GGML" - self.SELECT_LORA_ADAPTER_FILES = "Seleccionar archivos de adaptador LoRA" - self.ADD_ADAPTER = "Añadir adaptador" - self.DELETE_ADAPTER = "Eliminar" - self.LORA_SCALE = "Escala LoRA" - self.ENTER_LORA_SCALE_VALUE = "Ingresar valor de escala LoRA (Opcional)" - self.NUMBER_OF_THREADS_FOR_LORA_EXPORT = "Número de hilos para exportación LoRA" self.EXPORTING_LORA = "Exportando LoRA..." self.BROWSING_FOR_EXPORT_LORA_MODEL_FILE = ( - "Buscando archivo de modelo LoRA para exportar..." + "Explorando archivo de modelo para exportación LoRA..." ) self.BROWSING_FOR_EXPORT_LORA_OUTPUT_FILE = ( - "Buscando archivo de salida LoRA para exportar..." + "Explorando archivo de salida para exportación LoRA..." ) self.ADDING_LORA_ADAPTER = "Añadiendo adaptador LoRA..." self.DELETING_LORA_ADAPTER = "Eliminando adaptador LoRA..." - self.LORA_FILES = "Archivos LoRA (*.bin)" self.SELECT_LORA_ADAPTER_FILE = "Seleccionar archivo de adaptador LoRA" self.STARTING_LORA_EXPORT = "Iniciando exportación LoRA..." - self.OUTPUT_TYPE = "Tipo de salida" self.SELECT_OUTPUT_TYPE = "Seleccionar tipo de salida (GGUF o GGML)" - self.GGUF_AND_BIN_FILES = "Archivos GGUF y binarios (*.gguf *.bin)" self.BASE_MODEL = "Modelo base" self.SELECT_BASE_MODEL_FILE = "Seleccionar archivo de modelo base (GGUF)" self.BASE_MODEL_PATH_REQUIRED = ( "Se requiere la ruta del modelo base para la salida GGUF." ) - self.BROWSING_FOR_BASE_MODEL_FILE = "Buscando archivo de modelo base..." + self.BROWSING_FOR_BASE_MODEL_FILE = "Explorando archivo de modelo base..." self.SELECT_BASE_MODEL_FOLDER = ( "Seleccionar carpeta de modelo base (que contiene safetensors)" ) - self.BROWSING_FOR_BASE_MODEL_FOLDER = "Buscando carpeta de modelo base..." + self.BROWSING_FOR_BASE_MODEL_FOLDER = "Explorando carpeta de modelo base..." self.LORA_CONVERSION_FROM_TO = "Conversión LoRA de {} a {}" self.GENERATING_IMATRIX_FOR = "Generando IMatrix para {}" self.MODEL_PATH_REQUIRED_FOR_IMATRIX = ( "Se requiere la ruta del modelo para la generación de IMatrix." ) + self.NO_ASSET_SELECTED_FOR_CUDA_CHECK = ( + "No se seleccionó ningún asset para la verificación CUDA" + ) + self.NO_QUANTIZATION_TYPE_SELECTED = "No se seleccionó ningún tipo de cuantificación. Por favor, seleccione al menos un tipo de cuantificación." + self.STARTING_HF_TO_GGUF_CONVERSION = ( + "Iniciando conversión de HuggingFace a GGUF" + ) + self.MODEL_DIRECTORY_REQUIRED = "Se requiere el directorio del modelo" + self.HF_TO_GGUF_CONVERSION_COMMAND = "Comando de conversión de HF a GGUF: {}" + self.CONVERTING_TO_GGUF = "Convirtiendo {} a GGUF" + self.ERROR_STARTING_HF_TO_GGUF_CONVERSION = ( + "Error al iniciar la conversión de HuggingFace a GGUF: {}" + ) + self.HF_TO_GGUF_CONVERSION_TASK_STARTED = ( + "Tarea de conversión de HuggingFace a GGUF iniciada" + ) class _Hindi(_Localization): @@ -6197,6 +6120,12 @@ def set_language(lang_code): global BASE_MODEL_PATH_REQUIRED, BROWSING_FOR_BASE_MODEL_FILE, SELECT_BASE_MODEL_FOLDER, BROWSING_FOR_BASE_MODEL_FOLDER global LORA_CONVERSION_FROM_TO, GENERATING_IMATRIX_FOR, MODEL_PATH_REQUIRED_FOR_IMATRIX, NO_ASSET_SELECTED_FOR_CUDA_CHECK, QUANTIZATION_COMMAND global IMATRIX_GENERATION_COMMAND, LORA_CONVERSION_COMMAND, LORA_EXPORT_COMMAND + global NO_QUANTIZATION_TYPE_SELECTED, STARTING_HF_TO_GGUF_CONVERSION, MODEL_DIRECTORY_REQUIRED + global HF_TO_GGUF_CONVERSION_COMMAND, CONVERTING_TO_GGUF, ERROR_STARTING_HF_TO_GGUF_CONVERSION + global HF_TO_GGUF_CONVERSION_TASK_STARTED, HF_TO_GGUF_CONVERSION, MODEL_DIRECTORY, OUTPUT_FILE + global VOCAB_ONLY, USE_TEMP_FILE, NO_LAZY_EVALUATION, MODEL_NAME, VERBOSE, SPLIT_MAX_SIZE + global DRY_RUN, CONVERT_HF_TO_GGUF, SELECT_HF_MODEL_DIRECTORY, BROWSE_FOR_HF_MODEL_DIRECTORY + global BROWSE_FOR_HF_TO_GGUF_OUTPUT, SHARDED loc = _languages.get(lang_code, _English)() english_loc = _English() # Create an instance of English localization for fallback