From 000ca6bb1f2d8fc341c347fbb961b2e941748a4c Mon Sep 17 00:00:00 2001 From: BuildTools Date: Fri, 16 Aug 2024 15:22:48 -0700 Subject: [PATCH] ci: support 32-bit builds - support 32-bit builds - fix pre-commit formatting issues --- .github/workflows/build.yml | 16 +++- src/localizations.py | 186 ++++++++++++++---------------------- 2 files changed, 81 insertions(+), 121 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 4e035ac..b321c4a 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -17,6 +17,10 @@ jobs: strategy: matrix: os: [windows-latest, ubuntu-latest, macos-latest] + arch: [x64] + include: + - os: windows-latest + arch: x86 runs-on: ${{ matrix.os }} steps: @@ -26,6 +30,7 @@ jobs: uses: actions/setup-python@v2 with: python-version: '3.x' + architecture: ${{ matrix.arch }} - name: Install dependencies run: | @@ -36,19 +41,20 @@ jobs: - name: Build with PyInstaller (Windows) if: matrix.os == 'windows-latest' run: | + $archSuffix = if ("${{ matrix.arch }}" -eq "x86") { "-x86" } else { "-x64" } if ("${{ github.event.inputs.build_type }}" -eq "RELEASE") { - pyinstaller --windowed --onefile --name=AutoGGUF --icon=../../assets/favicon_large.png --add-data "../../assets;assets" --distpath=build\release\dist --workpath=build\release\build --specpath=build\release src\main.py + pyinstaller --windowed --onefile --name=AutoGGUF$archSuffix --icon=../../assets/favicon_large.png --add-data "../../assets;assets" --distpath=build\release\dist --workpath=build\release\build --specpath=build\release src\main.py } else { - pyinstaller --onefile --name=AutoGGUF --icon=../../assets/favicon_large.png --add-data "../../assets;assets" --distpath=build\dev\dist --workpath=build\dev\build --specpath=build\dev src\main.py + pyinstaller --onefile --name=AutoGGUF$archSuffix --icon=../../assets/favicon_large.png --add-data "../../assets;assets" --distpath=build\dev\dist --workpath=build\dev\build --specpath=build\dev src\main.py } - name: Build with PyInstaller (Linux/macOS) if: matrix.os != 'windows-latest' run: | if [ "${{ github.event.inputs.build_type }}" = "RELEASE" ]; then - pyinstaller --windowed --onefile --name=AutoGGUF --icon=../../assets/favicon_large.png --add-data "../../assets:assets" --distpath=build/release/dist --workpath=build/release/build --specpath=build/release src/main.py + pyinstaller --windowed --onefile --name=AutoGGUF-x64 --icon=../../assets/favicon_large.png --add-data "../../assets:assets" --distpath=build/release/dist --workpath=build/release/build --specpath=build/release src/main.py else - pyinstaller --onefile --name=AutoGGUF --icon=../../assets/favicon_large.png --add-data "../../assets:assets" --distpath=build/dev/dist --workpath=build/dev/build --specpath=build/dev src/main.py + pyinstaller --onefile --name=AutoGGUF-x64 --icon=../../assets/favicon_large.png --add-data "../../assets:assets" --distpath=build/dev/dist --workpath=build/dev/build --specpath=build/dev src/main.py fi - name: Copy additional files (Windows) @@ -72,6 +78,6 @@ jobs: - name: Upload Artifact uses: actions/upload-artifact@v2 with: - name: AutoGGUF-${{ matrix.os }}-${{ github.event.inputs.build_type }}-${{ github.sha }} + name: AutoGGUF-${{ matrix.os }}-${{ matrix.arch }}-${{ github.event.inputs.build_type }}-${{ github.sha }} path: build/${{ github.event.inputs.build_type == 'RELEASE' && 'release' || 'dev' }}/dist diff --git a/src/localizations.py b/src/localizations.py index 7c88132..2497b2a 100644 --- a/src/localizations.py +++ b/src/localizations.py @@ -877,9 +877,7 @@ 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" @@ -943,9 +941,7 @@ 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" @@ -992,9 +988,7 @@ 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" @@ -1442,9 +1436,7 @@ 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" @@ -1473,9 +1465,7 @@ 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" ) @@ -1497,17 +1487,25 @@ 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" @@ -1534,7 +1532,9 @@ 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}" @@ -1544,14 +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.DISABLE_K_QUANT_MIXTURES = ( - "k-quant मिश्रण को अक्षम करें और सभी टेंसर को एक ही प्रकार में क्वांटाइज़ करें" + self.LEAVE_OUTPUT_WEIGHT = ( + "output.weight को अक्वांटाइज़ (या पुनः क्वांटाइज़) छोड़ देगा" ) - self.USE_DATA_AS_IMPORTANCE_MATRIX = ( - "क्वांट अनुकूलन के लिए फ़ाइल में डेटा को महत्व मैट्रिक्स के रूप में उपयोग 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_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" ) @@ -2008,9 +2008,7 @@ 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" @@ -2023,11 +2021,11 @@ 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.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.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.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" @@ -2052,10 +2050,10 @@ 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.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_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.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" ) @@ -2105,42 +2103,24 @@ 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" @@ -2797,11 +2777,11 @@ 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.CUDA_FILES_EXTRACTED = "CUDA 파일이 에 추출되었습니다." - self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = ( - "추출에 적합한 CUDA 백엔드를 찾을 수 없습니다." + 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.ERROR_FETCHING_RELEASES = "릴리스를 가져오는 중 오류가 발생했습니다: {0}" self.CONFIRM_DELETION_TITLE = "삭제 확인" self.LOG_FOR = "{0}에 대한 로그" @@ -2825,13 +2805,11 @@ 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.NO_SUITABLE_CUDA_BACKEND_FOUND = ( - "추출에 적합한 CUDA 백엔드를 찾을 수 없습니다." + 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.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = ( "llama.cpp 바이너리가 다운로드되어 {0}에 추출되었습니다." ) @@ -2868,14 +2846,10 @@ 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." @@ -2885,9 +2859,7 @@ 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}" @@ -2896,26 +2868,14 @@ 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." ) @@ -3870,7 +3830,9 @@ 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}" @@ -3878,13 +3840,11 @@ 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.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.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.USE_IMPORTANCE_MATRIX_FOR_TENSORS = ( "এই টেন্সরগুলির জন্য গুরুত্বপূর্ণ ম্যাট্রিক্স ব্যবহার করুন" ) @@ -5988,9 +5948,7 @@ 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" @@ -6047,18 +6005,14 @@ 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"