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AutoGGUF
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feat(ui): add menubar 

- add basic menu bar showing Close and About areas
- add program version in localizations.py
- refactor functions out of AutoGGUF.py and move to ui_update.py
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BuildTools 2024-08-16 15:15:29 -07:00
parent f5e0bca12a
commit c5e1313e9c
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3 changed files with 254 additions and 153 deletions
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122
src/AutoGGUF.py
View File

@ -5,6 +5,7 @@
import psutil
import requests
from functools import partial
from PySide6.QtCore import *
from PySide6.QtGui import *
from PySide6.QtWidgets import *
@ -19,6 +20,7 @@
from error_handling import show_error, handle_error
from imports_and_globals import ensure_directory, open_file_safe, resource_path
from localizations import *
from ui_update import *
class AutoGGUF(QMainWindow):
@ -34,6 +36,19 @@ def __init__(self):
ensure_directory(os.path.abspath("quantized_models"))
ensure_directory(os.path.abspath("models"))
# References
self.update_base_model_visibility = partial(update_base_model_visibility, self)
self.update_assets = update_assets.__get__(self)
self.update_cuda_option = update_cuda_option.__get__(self)
self.update_cuda_backends = update_cuda_backends.__get__(self)
self.update_threads_spinbox = partial(update_threads_spinbox, self)
self.update_threads_slider = partial(update_threads_slider, self)
self.update_gpu_offload_spinbox = partial(update_gpu_offload_spinbox, self)
self.update_gpu_offload_slider = partial(update_gpu_offload_slider, self)
self.update_model_info = partial(update_model_info, self.logger, self)
self.update_system_info = partial(update_system_info, self)
self.update_download_progress = partial(update_download_progress, self)
# Create a central widget and main layout
central_widget = QWidget()
main_layout = QHBoxLayout(central_widget)
@ -52,6 +67,23 @@ def __init__(self):
left_widget.setMinimumWidth(800)
right_widget.setMinimumWidth(400)
menubar = QMenuBar(self)
self.layout().setMenuBar(menubar)
# File menu
file_menu = menubar.addMenu("&File")
close_action = QAction("&Close", self)
close_action.setShortcut(QKeySequence.Quit)
close_action.triggered.connect(self.close)
file_menu.addAction(close_action)
# Help menu
help_menu = menubar.addMenu("&Help")
about_action = QAction("&About", self)
about_action.setShortcut(QKeySequence("Ctrl+Q"))
about_action.triggered.connect(self.show_about)
help_menu.addAction(about_action)
left_layout = QVBoxLayout(left_widget)
right_layout = QVBoxLayout(right_widget)
@ -679,9 +711,13 @@ def refresh_backends(self):
self.backend_combo.setEnabled(False)
self.logger.info(FOUND_VALID_BACKENDS.format(self.backend_combo.count()))
def update_base_model_visibility(self, index):
is_gguf = self.lora_output_type_combo.itemText(index) == "GGUF"
self.base_model_wrapper.setVisible(is_gguf)
def show_about(self):
about_text = (
"AutoGGUF\n\n"
f"Version: {AUTOGGUF_VERSION}\n\n"
"A tool for managing and converting GGUF models."
)
QMessageBox.about(self, "About AutoGGUF", about_text)
def save_preset(self):
self.logger.info(SAVING_PRESET)
@ -1174,20 +1210,6 @@ def refresh_releases(self):
except requests.exceptions.RequestException as e:
show_error(self.logger, 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:
show_error(
self.logger, 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()
@ -1209,45 +1231,6 @@ def download_llama_cpp(self):
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)
@ -1335,18 +1318,6 @@ def show_task_properties(self, item):
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)
@ -1483,17 +1454,6 @@ def validate_quantization_inputs(self):
if errors:
raise ValueError("\n".join(errors))
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 add_kv_override(self, override_string=None):
entry = KVOverrideEntry()
entry.deleted.connect(self.remove_kv_override)
@ -1679,10 +1639,6 @@ def quantize_model(self):
except Exception as e:
show_error(self.logger, ERROR_STARTING_QUANTIZATION.format(str(e)))
def update_model_info(self, model_info):
self.logger.debug(UPDATING_MODEL_INFO.format(model_info))
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)

188
src/localizations.py
View File

@ -1,5 +1,7 @@
import os
AUTOGGUF_VERSION = "v1.6.2"
class _Localization:
def __init__(self):
@ -875,7 +877,9 @@ def __init__(self):
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解压到{0}"
self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = (
"llama.cpp二进制文件已下载并解压到{0}"
)
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"
@ -939,7 +943,9 @@ def __init__(self):
self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "对output.weight张量使用此类型"
self.TOKEN_EMBEDDING_TYPE = "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 = "将生成与输入相同分片的量化模型"
self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = (
"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"
self.MODEL_TO_BE_QUANTIZED = "要量化的模型"
@ -986,7 +992,9 @@ def __init__(self):
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.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"
@ -1434,7 +1442,9 @@ def __init__(self):
self.NO_MODEL_SELECTED = "कोई मॉडल चयनित नहीं"
self.REFRESH_RELEASES = "रिलीज़ रीफ्रेश करें"
self.NO_SUITABLE_CUDA_BACKENDS = "कोई उपयुक्त CUDA बैकएंड नहीं मिला"
self.LLAMACPP_DOWNLOADED_EXTRACTED = "llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं"
self.LLAMACPP_DOWNLOADED_EXTRACTED = (
"llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं"
)
self.CUDA_FILES_EXTRACTED = "CUDA फ़ाइलें निकाली गईं"
self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = (
"निष्कर्षण के लिए कोई उपयुक्त CUDA बैकएंड नहीं मिला"
@ -1463,7 +1473,9 @@ def __init__(self):
self.RESTARTING_TASK = "कार्य पुनः आरंभ हो रहा है: {0}"
self.IN_PROGRESS = "प्रगति में"
self.DOWNLOAD_FINISHED_EXTRACTED_TO = "डाउनलोड समाप्त। निकाला गया: {0}"
self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं"
self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = (
"llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं"
)
self.NO_SUITABLE_CUDA_BACKEND_FOUND = (
"निष्कर्षण के लिए कोई उपयुक्त CUDA बैकएंड नहीं मिला"
)
@ -1485,25 +1497,17 @@ def __init__(self):
self.DELETING_TASK = "कार्य हटाया जा रहा है: {0}"
self.LOADING_MODELS = "मॉडल लोड हो रहे हैं"
self.LOADED_MODELS = "{0} मॉडल लोड किए गए"
self.BROWSING_FOR_MODELS_DIRECTORY = (
"मॉडल निर्देशिका के लिए ब्राउज़ किया जा रहा है"
)
self.BROWSING_FOR_MODELS_DIRECTORY = "मॉडल निर्देशिका के लिए ब्राउज़ किया जा रहा है"
self.SELECT_MODELS_DIRECTORY = "मॉडल निर्देशिका चुनें"
self.BROWSING_FOR_OUTPUT_DIRECTORY = (
"आउटपुट निर्देशिका के लिए ब्राउज़ किया जा रहा है"
)
self.BROWSING_FOR_OUTPUT_DIRECTORY = "आउटपुट निर्देशिका के लिए ब्राउज़ किया जा रहा है"
self.SELECT_OUTPUT_DIRECTORY = "आउटपुट निर्देशिका चुनें"
self.BROWSING_FOR_LOGS_DIRECTORY = (
"लॉग निर्देशिका के लिए ब्राउज़ किया जा रहा है"
)
self.BROWSING_FOR_LOGS_DIRECTORY = "लॉग निर्देशिका के लिए ब्राउज़ किया जा रहा है"
self.SELECT_LOGS_DIRECTORY = "लॉग निर्देशिका चुनें"
self.BROWSING_FOR_IMATRIX_FILE = "IMatrix फ़ाइल के लिए ब्राउज़ किया जा रहा है"
self.SELECT_IMATRIX_FILE = "IMatrix फ़ाइल चुनें"
self.RAM_USAGE_FORMAT = "{0:.1f}% ({1} MB / {2} MB)"
self.CPU_USAGE_FORMAT = "CPU उपयोग: {0:.1f}%"
self.VALIDATING_QUANTIZATION_INPUTS = (
"क्वांटाइजेशन इनपुट सत्यापित किए जा रहे हैं"
)
self.VALIDATING_QUANTIZATION_INPUTS = "क्वांटाइजेशन इनपुट सत्यापित किए जा रहे हैं"
self.MODELS_PATH_REQUIRED = "मॉडल पथ आवश्यक है"
self.OUTPUT_PATH_REQUIRED = "आउटपुट पथ आवश्यक है"
self.LOGS_PATH_REQUIRED = "लॉग पथ आवश्यक है"
@ -1530,9 +1534,7 @@ def __init__(self):
self.STARTING_IMATRIX_GENERATION = "IMatrix उत्पादन शुरू हो रहा है"
self.BACKEND_PATH_NOT_EXIST = "बैकएंड पथ मौजूद नहीं है: {0}"
self.GENERATING_IMATRIX = "IMatrix उत्पन्न किया जा रहा है"
self.ERROR_STARTING_IMATRIX_GENERATION = (
"IMatrix उत्पादन शुरू करने में त्रुटि: {0}"
)
self.ERROR_STARTING_IMATRIX_GENERATION = "IMatrix उत्पादन शुरू करने में त्रुटि: {0}"
self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix उत्पादन कार्य शुरू हुआ"
self.ERROR_MESSAGE = "त्रुटि: {0}"
self.TASK_ERROR = "कार्य त्रुटि: {0}"
@ -1542,14 +1544,14 @@ def __init__(self):
self.ALLOWS_REQUANTIZING = (
"पहले से क्वांटाइज़ किए गए टेंसर को पुनः क्वांटाइज़ करने की अनुमति देता है"
)
self.LEAVE_OUTPUT_WEIGHT = (
"output.weight को अक्वांटाइज़ (या पुनः क्वांटाइज़) छोड़ देगा"
self.LEAVE_OUTPUT_WEIGHT = "output.weight को अक्वांटाइज़ (या पुनः क्वांटाइज़) छोड़ देगा"
self.DISABLE_K_QUANT_MIXTURES = (
"k-quant मिश्रण को अक्षम करें और सभी टेंसर को एक ही प्रकार में क्वांटाइज़ करें"
)
self.DISABLE_K_QUANT_MIXTURES = "k-quant मिश्रण को अक्षम करें और सभी टेंसर को एक ही प्रकार में क्वांटाइज़ करें"
self.USE_DATA_AS_IMPORTANCE_MATRIX = "क्वांट अनुकूलन के लिए फ़ाइल में डेटा को महत्व मैट्रिक्स के रूप में उपयोग करें"
self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
"इन टेंसर के लिए महत्व मैट्रिक्स का उपयोग करें"
self.USE_DATA_AS_IMPORTANCE_MATRIX = (
"क्वांट अनुकूलन के लिए फ़ाइल में डेटा को महत्व मैट्रिक्स के रूप में उपयोग करें"
)
self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "इन टेंसर के लिए महत्व मैट्रिक्स का उपयोग करें"
self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
"इन टेंसर के लिए महत्व मैट्रिक्स का उपयोग न करें"
)
@ -2006,7 +2008,9 @@ def __init__(self):
self.RESTART = "再起動"
self.DELETE = "削除"
self.CONFIRM_DELETION = "このタスクを削除してもよろしいですか？"
self.TASK_RUNNING_WARNING = "一部のタスクはまだ実行中です。終了してもよろしいですか？"
self.TASK_RUNNING_WARNING = (
"一部のタスクはまだ実行中です。終了してもよろしいですか？"
)
self.YES = "はい"
self.NO = "いいえ"
self.DOWNLOAD_COMPLETE = "ダウンロード完了"
@ -2019,11 +2023,11 @@ def __init__(self):
self.NO_MODEL_SELECTED = "モデルが選択されていません"
self.REFRESH_RELEASES = "リリースを更新"
self.NO_SUITABLE_CUDA_BACKENDS = "適切なCUDAバックエンドが見つかりませんでした"
self.LLAMACPP_DOWNLOADED_EXTRACTED = (
"llama.cppバイナリがダウンロードされ、{0}に抽出されました\nCUDAファイルは{1}に抽出されました"
)
self.LLAMACPP_DOWNLOADED_EXTRACTED = "llama.cppバイナリがダウンロードされ、{0}に抽出されました\nCUDAファイルは{1}に抽出されました"
self.CUDA_FILES_EXTRACTED = "CUDAファイルはに抽出されました"
self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = "抽出に適したCUDAバックエンドが見つかりませんでした"
self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = (
"抽出に適したCUDAバックエンドが見つかりませんでした"
)
self.ERROR_FETCHING_RELEASES = "リリースの取得中にエラーが発生しました: {0}"
self.CONFIRM_DELETION_TITLE = "削除の確認"
self.LOG_FOR = "{0}のログ"
@ -2048,10 +2052,10 @@ def __init__(self):
self.RESTARTING_TASK = "タスクを再起動しています: {0}"
self.IN_PROGRESS = "処理中"
self.DOWNLOAD_FINISHED_EXTRACTED_TO = "ダウンロードが完了しました。抽出先: {0}"
self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = (
"llama.cppバイナリがダウンロードされ、{0}に抽出されました\nCUDAファイルは{1}に抽出されました"
self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "llama.cppバイナリがダウンロードされ、{0}に抽出されました\nCUDAファイルは{1}に抽出されました"
self.NO_SUITABLE_CUDA_BACKEND_FOUND = (
"抽出に適したCUDAバックエンドが見つかりませんでした"
)
self.NO_SUITABLE_CUDA_BACKEND_FOUND = "抽出に適したCUDAバックエンドが見つかりませんでした"
self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = (
"llama.cppバイナリがダウンロードされ、{0}に抽出されました"
)
@ -2101,24 +2105,42 @@ def __init__(self):
self.STARTING_IMATRIX_GENERATION = "IMatrixの生成を開始しています"
self.BACKEND_PATH_NOT_EXIST = "バックエンドパスが存在しません: {0}"
self.GENERATING_IMATRIX = "IMatrixを生成しています"
self.ERROR_STARTING_IMATRIX_GENERATION = "IMatrixの生成を開始中にエラーが発生しました: {0}"
self.ERROR_STARTING_IMATRIX_GENERATION = (
"IMatrixの生成を開始中にエラーが発生しました: {0}"
)
self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix生成タスクが開始されました"
self.ERROR_MESSAGE = "エラー: {0}"
self.TASK_ERROR = "タスクエラー: {0}"
self.APPLICATION_CLOSING = "アプリケーションを終了しています"
self.APPLICATION_CLOSED = "アプリケーションが終了しました"
self.SELECT_QUANTIZATION_TYPE = "量子化タイプを選択してください"
self.ALLOWS_REQUANTIZING = "すでに量子化されているテンソルの再量子化を許可します"
self.ALLOWS_REQUANTIZING = (
"すでに量子化されているテンソルの再量子化を許可します"
)
self.LEAVE_OUTPUT_WEIGHT = "output.weightは（再）量子化されません"
self.DISABLE_K_QUANT_MIXTURES = "k-quant混合を無効にし、すべてのテンソルを同じタイプに量子化します"
self.USE_DATA_AS_IMPORTANCE_MATRIX = "量子化最適化の重要度マトリックスとしてファイル内のデータを使用します"
self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "これらのテンソルに重要度マトリックスを使用します"
self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = "これらのテンソルに重要度マトリックスを使用しません"
self.DISABLE_K_QUANT_MIXTURES = (
"k-quant混合を無効にし、すべてのテンソルを同じタイプに量子化します"
)
self.USE_DATA_AS_IMPORTANCE_MATRIX = (
"量子化最適化の重要度マトリックスとしてファイル内のデータを使用します"
)
self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
"これらのテンソルに重要度マトリックスを使用します"
)
self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
"これらのテンソルに重要度マトリックスを使用しません"
)
self.OUTPUT_TENSOR_TYPE = "出力テンソルタイプ:"
self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "output.weightテンソルにこのタイプを使用します"
self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = (
"output.weightテンソルにこのタイプを使用します"
)
self.TOKEN_EMBEDDING_TYPE = "トークン埋め込みタイプ:"
self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = "トークン埋め込みテンソルにこのタイプを使用します"
self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = "入力と同じシャードで量子化されたモデルを生成します"
self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = (
"トークン埋め込みテンソルにこのタイプを使用します"
)
self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = (
"入力と同じシャードで量子化されたモデルを生成します"
)
self.OVERRIDE_MODEL_METADATA = "モデルメタデータを上書きする"
self.INPUT_DATA_FILE_FOR_IMATRIX = "IMatrix生成用の入力データファイル"
self.MODEL_TO_BE_QUANTIZED = "量子化されるモデル"
@ -2775,11 +2797,11 @@ def __init__(self):
self.NO_MODEL_SELECTED = "모델이 선택되지 않았습니다"
self.REFRESH_RELEASES = "릴리스 새로 고침"
self.NO_SUITABLE_CUDA_BACKENDS = "적합한 CUDA 백엔드를 찾을 수 없습니다"
self.LLAMACPP_DOWNLOADED_EXTRACTED = (
"llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다.\nCUDA 파일이 {1}에 추출되었습니다."
)
self.LLAMACPP_DOWNLOADED_EXTRACTED = "llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다.\nCUDA 파일이 {1}에 추출되었습니다."
self.CUDA_FILES_EXTRACTED = "CUDA 파일이 에 추출되었습니다."
self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = "추출에 적합한 CUDA 백엔드를 찾을 수 없습니다."
self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = (
"추출에 적합한 CUDA 백엔드를 찾을 수 없습니다."
)
self.ERROR_FETCHING_RELEASES = "릴리스를 가져오는 중 오류가 발생했습니다: {0}"
self.CONFIRM_DELETION_TITLE = "삭제 확인"
self.LOG_FOR = "{0}에 대한 로그"
@ -2803,11 +2825,13 @@ def __init__(self):
self.TASK_PRESET_SAVED_TO = "작업 프리셋이 {0}에 저장되었습니다."
self.RESTARTING_TASK = "작업을 다시 시작하는 중입니다: {0}"
self.IN_PROGRESS = "진행 중"
self.DOWNLOAD_FINISHED_EXTRACTED_TO = "다운로드가 완료되었습니다. 추출 위치: {0}"
self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = (
"llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다.\nCUDA 파일이 {1}에 추출되었습니다."
self.DOWNLOAD_FINISHED_EXTRACTED_TO = (
"다운로드가 완료되었습니다. 추출 위치: {0}"
)
self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다.\nCUDA 파일이 {1}에 추출되었습니다."
self.NO_SUITABLE_CUDA_BACKEND_FOUND = (
"추출에 적합한 CUDA 백엔드를 찾을 수 없습니다."
)
self.NO_SUITABLE_CUDA_BACKEND_FOUND = "추출에 적합한 CUDA 백엔드를 찾을 수 없습니다."
self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = (
"llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다."
)
@ -2844,10 +2868,14 @@ def __init__(self):
self.INPUT_FILE_NOT_EXIST = "입력 파일 '{0}'이 존재하지 않습니다."
self.QUANTIZING_MODEL_TO = "{0}을 {1}(으)로 양자화하는 중입니다."
self.QUANTIZATION_TASK_STARTED = "{0}에 대한 양자화 작업이 시작되었습니다."
self.ERROR_STARTING_QUANTIZATION = "양자화를 시작하는 중 오류가 발생했습니다: {0}"
self.ERROR_STARTING_QUANTIZATION = (
"양자화를 시작하는 중 오류가 발생했습니다: {0}"
)
self.UPDATING_MODEL_INFO = "모델 정보를 업데이트하는 중입니다: {0}"
self.TASK_FINISHED = "작업이 완료되었습니다: {0}"
self.SHOWING_TASK_DETAILS_FOR = "다음에 대한 작업 세부 정보를 표시하는 중입니다: {0}"
self.SHOWING_TASK_DETAILS_FOR = (
"다음에 대한 작업 세부 정보를 표시하는 중입니다: {0}"
)
self.BROWSING_FOR_IMATRIX_DATA_FILE = "IMatrix 데이터 파일을 찾아보는 중입니다."
self.SELECT_DATA_FILE = "데이터 파일 선택"
self.BROWSING_FOR_IMATRIX_MODEL_FILE = "IMatrix 모델 파일을 찾아보는 중입니다."
@ -2857,7 +2885,9 @@ def __init__(self):
self.STARTING_IMATRIX_GENERATION = "IMatrix 생성을 시작하는 중입니다."
self.BACKEND_PATH_NOT_EXIST = "백엔드 경로가 존재하지 않습니다: {0}"
self.GENERATING_IMATRIX = "IMatrix를 생성하는 중입니다."
self.ERROR_STARTING_IMATRIX_GENERATION = "IMatrix 생성을 시작하는 중 오류가 발생했습니다: {0}"
self.ERROR_STARTING_IMATRIX_GENERATION = (
"IMatrix 생성을 시작하는 중 오류가 발생했습니다: {0}"
)
self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix 생성 작업이 시작되었습니다."
self.ERROR_MESSAGE = "오류: {0}"
self.TASK_ERROR = "작업 오류: {0}"
@ -2866,14 +2896,26 @@ def __init__(self):
self.SELECT_QUANTIZATION_TYPE = "양자화 유형을 선택하세요."
self.ALLOWS_REQUANTIZING = "이미 양자화된 텐서의 재양자화를 허용합니다."
self.LEAVE_OUTPUT_WEIGHT = "output.weight를 (재)양자화하지 않은 상태로 둡니다."
self.DISABLE_K_QUANT_MIXTURES = "k-양자 혼합을 비활성화하고 모든 텐서를 동일한 유형으로 양자화합니다."
self.USE_DATA_AS_IMPORTANCE_MATRIX = "양자 최적화를 위한 중요도 행렬로 파일의 데이터를 사용합니다."
self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "이러한 텐서에 중요도 행렬을 사용합니다."
self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = "이러한 텐서에 중요도 행렬을 사용하지 않습니다."
self.DISABLE_K_QUANT_MIXTURES = (
"k-양자 혼합을 비활성화하고 모든 텐서를 동일한 유형으로 양자화합니다."
)
self.USE_DATA_AS_IMPORTANCE_MATRIX = (
"양자 최적화를 위한 중요도 행렬로 파일의 데이터를 사용합니다."
)
self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
"이러한 텐서에 중요도 행렬을 사용합니다."
)
self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
"이러한 텐서에 중요도 행렬을 사용하지 않습니다."
)
self.OUTPUT_TENSOR_TYPE = "출력 텐서 유형:"
self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "output.weight 텐서에 이 유형을 사용합니다."
self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = (
"output.weight 텐서에 이 유형을 사용합니다."
)
self.TOKEN_EMBEDDING_TYPE = "토큰 임베딩 유형:"
self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = "토큰 임베딩 텐서에 이 유형을 사용합니다."
self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = (
"토큰 임베딩 텐서에 이 유형을 사용합니다."
)
self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = (
"입력과 동일한 샤드에 양자화된 모델을 생성합니다."
)
@ -3828,9 +3870,7 @@ def __init__(self):
self.STARTING_IMATRIX_GENERATION = "IMatrix জেনারেশন শুরু হচ্ছে"
self.BACKEND_PATH_NOT_EXIST = "ব্যাকএন্ড পাথ বিদ্যমান নেই: {0}"
self.GENERATING_IMATRIX = "IMatrix তৈরি করা হচ্ছে"
self.ERROR_STARTING_IMATRIX_GENERATION = (
"IMatrix জেনারেশন শুরু করতে ত্রুটি: {0}"
)
self.ERROR_STARTING_IMATRIX_GENERATION = "IMatrix জেনারেশন শুরু করতে ত্রুটি: {0}"
self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix জেনারেশন টাস্ক শুরু হয়েছে"
self.ERROR_MESSAGE = "ত্রুটি: {0}"
self.TASK_ERROR = "টাস্ক ত্রুটি: {0}"
@ -3838,11 +3878,13 @@ def __init__(self):
self.APPLICATION_CLOSED = "অ্যাপ্লিকেশন বন্ধ"
self.SELECT_QUANTIZATION_TYPE = "কোয়ান্টাইজেশন ধরণ নির্বাচন করুন"
self.ALLOWS_REQUANTIZING = "যে টেন্সরগুলি ইতিমধ্যে কোয়ান্টাইজ করা হয়েছে তাদের পুনরায় কোয়ান্টাইজ করার অনুমতি দেয়"
self.LEAVE_OUTPUT_WEIGHT = (
"output.weight কে (পুনরায়) কোয়ান্টাইজ না করে রেখে দেবে"
self.LEAVE_OUTPUT_WEIGHT = "output.weight কে (পুনরায়) কোয়ান্টাইজ না করে রেখে দেবে"
self.DISABLE_K_QUANT_MIXTURES = (
"k-কোয়ান্ট মিশ্রণগুলি অক্ষম করুন এবং সমস্ত টেন্সরকে একই ধরণের কোয়ান্টাইজ করুন"
)
self.USE_DATA_AS_IMPORTANCE_MATRIX = (
"কোয়ান্ট অপ্টিমাইজেশনের জন্য ফাইলের ডেটা গুরুত্বপূর্ণ ম্যাট্রিক্স হিসাবে ব্যবহার করুন"
)
self.DISABLE_K_QUANT_MIXTURES = "k-কোয়ান্ট মিশ্রণগুলি অক্ষম করুন এবং সমস্ত টেন্সরকে একই ধরণের কোয়ান্টাইজ করুন"
self.USE_DATA_AS_IMPORTANCE_MATRIX = "কোয়ান্ট অপ্টিমাইজেশনের জন্য ফাইলের ডেটা গুরুত্বপূর্ণ ম্যাট্রিক্স হিসাবে ব্যবহার করুন"
self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
"এই টেন্সরগুলির জন্য গুরুত্বপূর্ণ ম্যাট্রিক্স ব্যবহার করুন"
)
@ -5946,7 +5988,9 @@ def __init__(self):
"llama.cpp 二進位檔案已下載並解壓縮至 {0}\nCUDA 檔案已解壓縮至 {1}"
)
self.NO_SUITABLE_CUDA_BACKEND_FOUND = "找不到合適的 CUDA 後端進行解壓縮"
self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = "llama.cpp 二進位檔案已下載並解壓縮至 {0}"
self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = (
"llama.cpp 二進位檔案已下載並解壓縮至 {0}"
)
self.REFRESHING_LLAMACPP_RELEASES = "正在重新整理 llama.cpp 版本"
self.UPDATING_ASSET_LIST = "正在更新資源清單"
self.UPDATING_CUDA_OPTIONS = "正在更新 CUDA 選項"
@ -6003,14 +6047,18 @@ def __init__(self):
self.ALLOWS_REQUANTIZING = "允許重新量化已量化的張量"
self.LEAVE_OUTPUT_WEIGHT = "將保留 output.weight 不被（重新）量化"
self.DISABLE_K_QUANT_MIXTURES = "停用 k-quant 混合並將所有張量量化為相同類型"
self.USE_DATA_AS_IMPORTANCE_MATRIX = "使用檔案中的資料作為量化最佳化的重要性矩陣"
self.USE_DATA_AS_IMPORTANCE_MATRIX = (
"使用檔案中的資料作為量化最佳化的重要性矩陣"
)
self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "對這些張量使用重要性矩陣"
self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = "不要對這些張量使用重要性矩陣"
self.OUTPUT_TENSOR_TYPE = "輸出張量類型："
self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "對 output.weight 張量使用此類型"
self.TOKEN_EMBEDDING_TYPE = "權杖嵌入類型："
self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = "對權杖嵌入張量使用此類型"
self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = "將在與輸入相同的分片中產生量化模型"
self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = (
"將在與輸入相同的分片中產生量化模型"
)
self.OVERRIDE_MODEL_METADATA = "覆蓋模型中繼資料"
self.INPUT_DATA_FILE_FOR_IMATRIX = "IMatrix 產生的輸入資料檔案"
self.MODEL_TO_BE_QUANTIZED = "要量化的模型"

97
src/ui_update.py Normal file
View File

@ -0,0 +1,97 @@
from localizations import *
import psutil
def update_model_info(logger, self, model_info):
logger.debug(UPDATING_MODEL_INFO.format(model_info))
pass
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 update_download_progress(self, progress):
self.download_progress.setValue(progress)
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_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 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_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:
show_error(
self.logger, NO_ASSETS_FOUND_FOR_RELEASE.format(release["tag_name"])
)
self.update_cuda_option()
def update_base_model_visibility(self, index):
is_gguf = self.lora_output_type_combo.itemText(index) == "GGUF"
self.base_model_wrapper.setVisible(is_gguf)