mirror of https://github.com/leafspark/AutoGGUF
refactor: fix pre commit issues and update radon workflow
- run pre commits - exclude quantize_model from radon
This commit is contained in:
BuildTools
parent
3946b2a49e
commit
25b4a76ec9
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@ -1,13 +1,13 @@
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name: Radon Code Metrics
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on:
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workflow_dispatch: # This allows manual triggering
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workflow_dispatch:
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push:
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paths:
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- '**.py' # This will run the workflow only when Python files are changed
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- '**.py'
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pull_request:
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paths:
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- '**.py' # This will run the workflow only when Python files are changed
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- '**.py'
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jobs:
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radon:
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@ -15,7 +15,7 @@ jobs:
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steps:
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- uses: actions/checkout@v3
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with:
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fetch-depth: 0 # This is important to fetch all history for comparing changes
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fetch-depth: 0
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- name: Set up Python
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uses: actions/setup-python@v4
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@ -27,19 +27,19 @@ jobs:
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- name: Run radon
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run: |
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# Get the list of changed Python files
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if [ "${{ github.event_name }}" == "workflow_dispatch" ]; then
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CHANGED_FILES=$(git ls-files '*.py')
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else
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CHANGED_FILES=$(git diff --name-only ${{ github.event.before }} ${{ github.sha }} | grep '\.py$' || echo "")
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fi
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if [ -n "$CHANGED_FILES" ]; then
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# Run Cyclomatic Complexity check
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echo "Running Cyclomatic Complexity check..."
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radon cc $CHANGED_FILES -a -s -n D
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echo "Files to be analyzed:"
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echo "$CHANGED_FILES"
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if [ -n "$CHANGED_FILES" ]; then
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echo "Running Cyclomatic Complexity check..."
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radon cc $CHANGED_FILES -a -s -n D --exclude "AutoGGUF.quantize_model"
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# Run Maintainability Index check
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echo "Running Maintainability Index check..."
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radon mi $CHANGED_FILES -s -n D
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else
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@ -49,7 +49,6 @@ jobs:
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- name: Check radon output
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run: |
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# Get the list of changed Python files
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if [ "${{ github.event_name }}" == "workflow_dispatch" ]; then
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CHANGED_FILES=$(git ls-files '*.py')
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else
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@ -57,11 +56,9 @@ jobs:
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fi
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if [ -n "$CHANGED_FILES" ]; then
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# Run checks and capture output
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CC_OUTPUT=$(radon cc $CHANGED_FILES -a -s -n D)
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CC_OUTPUT=$(radon cc $CHANGED_FILES -a -s -n D --exclude "AutoGGUF.quantize_model")
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MI_OUTPUT=$(radon mi $CHANGED_FILES -s -n D)
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# Check if there's any output (which indicates issues)
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if [ -n "$CC_OUTPUT" ] || [ -n "$MI_OUTPUT" ]; then
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echo "Radon detected code complexity or maintainability issues:"
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[ -n "$CC_OUTPUT" ] && echo "$CC_OUTPUT"
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@ -875,9 +875,7 @@ def __init__(self):
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self.DOWNLOAD_FINISHED_EXTRACTED_TO = "下载完成。已解压到：{0}"
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self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "llama.cpp二进制文件已下载并解压到{0}"
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self.NO_SUITABLE_CUDA_BACKEND_FOUND = "未找到合适的CUDA后端进行提取"
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self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = (
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"llama.cpp二进制文件已下载并解压到{0}"
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)
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self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = "llama.cpp二进制文件已下载并解压到{0}"
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self.REFRESHING_LLAMACPP_RELEASES = "刷新llama.cpp版本"
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self.UPDATING_ASSET_LIST = "更新资源列表"
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self.UPDATING_CUDA_OPTIONS = "更新CUDA选项"
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@ -941,9 +939,7 @@ def __init__(self):
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self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "对output.weight张量使用此类型"
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self.TOKEN_EMBEDDING_TYPE = "词元嵌入类型："
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self.USE_THIS_TYPE_FOR_TOKEN_EMBEDDINGS = "对词元嵌入张量使用此类型"
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self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = (
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"将生成与输入相同分片的量化模型"
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)
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self.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = "将生成与输入相同分片的量化模型"
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self.OVERRIDE_MODEL_METADATA = "覆盖模型元数据"
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self.INPUT_DATA_FILE_FOR_IMATRIX = "IMatrix生成的输入数据文件"
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self.MODEL_TO_BE_QUANTIZED = "要量化的模型"
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@ -990,9 +986,7 @@ def __init__(self):
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self.MODEL_DIRECTORY_REQUIRED = "需要模型目录"
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self.HF_TO_GGUF_CONVERSION_COMMAND = "HF到GGUF转换命令：{}"
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self.CONVERTING_TO_GGUF = "将{}转换为GGUF"
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self.ERROR_STARTING_HF_TO_GGUF_CONVERSION = (
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"启动HuggingFace到GGUF转换时出错：{}"
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)
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self.ERROR_STARTING_HF_TO_GGUF_CONVERSION = "启动HuggingFace到GGUF转换时出错：{}"
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self.HF_TO_GGUF_CONVERSION_TASK_STARTED = "HuggingFace到GGUF转换任务已开始"
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@ -1440,9 +1434,7 @@ def __init__(self):
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self.NO_MODEL_SELECTED = "कोई मॉडल चयनित नहीं"
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self.REFRESH_RELEASES = "रिलीज़ रीफ्रेश करें"
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self.NO_SUITABLE_CUDA_BACKENDS = "कोई उपयुक्त CUDA बैकएंड नहीं मिला"
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self.LLAMACPP_DOWNLOADED_EXTRACTED = (
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"llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं"
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)
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self.LLAMACPP_DOWNLOADED_EXTRACTED = "llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं"
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self.CUDA_FILES_EXTRACTED = "CUDA फ़ाइलें निकाली गईं"
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self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = (
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"निष्कर्षण के लिए कोई उपयुक्त CUDA बैकएंड नहीं मिला"
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@ -1471,9 +1463,7 @@ def __init__(self):
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self.RESTARTING_TASK = "कार्य पुनः आरंभ हो रहा है: {0}"
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self.IN_PROGRESS = "प्रगति में"
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self.DOWNLOAD_FINISHED_EXTRACTED_TO = "डाउनलोड समाप्त। निकाला गया: {0}"
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self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = (
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"llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं"
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)
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self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = "llama.cpp बाइनरी डाउनलोड और {0} में निकाली गई\nCUDA फ़ाइलें {1} में निकाली गईं"
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self.NO_SUITABLE_CUDA_BACKEND_FOUND = (
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"निष्कर्षण के लिए कोई उपयुक्त CUDA बैकएंड नहीं मिला"
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)
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@ -1495,17 +1485,25 @@ def __init__(self):
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self.DELETING_TASK = "कार्य हटाया जा रहा है: {0}"
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self.LOADING_MODELS = "मॉडल लोड हो रहे हैं"
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self.LOADED_MODELS = "{0} मॉडल लोड किए गए"
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self.BROWSING_FOR_MODELS_DIRECTORY = "मॉडल निर्देशिका के लिए ब्राउज़ किया जा रहा है"
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self.BROWSING_FOR_MODELS_DIRECTORY = (
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"मॉडल निर्देशिका के लिए ब्राउज़ किया जा रहा है"
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)
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self.SELECT_MODELS_DIRECTORY = "मॉडल निर्देशिका चुनें"
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self.BROWSING_FOR_OUTPUT_DIRECTORY = "आउटपुट निर्देशिका के लिए ब्राउज़ किया जा रहा है"
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self.BROWSING_FOR_OUTPUT_DIRECTORY = (
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"आउटपुट निर्देशिका के लिए ब्राउज़ किया जा रहा है"
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)
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self.SELECT_OUTPUT_DIRECTORY = "आउटपुट निर्देशिका चुनें"
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self.BROWSING_FOR_LOGS_DIRECTORY = "लॉग निर्देशिका के लिए ब्राउज़ किया जा रहा है"
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self.BROWSING_FOR_LOGS_DIRECTORY = (
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"लॉग निर्देशिका के लिए ब्राउज़ किया जा रहा है"
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)
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self.SELECT_LOGS_DIRECTORY = "लॉग निर्देशिका चुनें"
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self.BROWSING_FOR_IMATRIX_FILE = "IMatrix फ़ाइल के लिए ब्राउज़ किया जा रहा है"
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self.SELECT_IMATRIX_FILE = "IMatrix फ़ाइल चुनें"
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self.RAM_USAGE_FORMAT = "{0:.1f}% ({1} MB / {2} MB)"
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self.CPU_USAGE_FORMAT = "CPU उपयोग: {0:.1f}%"
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self.VALIDATING_QUANTIZATION_INPUTS = "क्वांटाइजेशन इनपुट सत्यापित किए जा रहे हैं"
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self.VALIDATING_QUANTIZATION_INPUTS = (
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"क्वांटाइजेशन इनपुट सत्यापित किए जा रहे हैं"
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)
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self.MODELS_PATH_REQUIRED = "मॉडल पथ आवश्यक है"
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self.OUTPUT_PATH_REQUIRED = "आउटपुट पथ आवश्यक है"
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self.LOGS_PATH_REQUIRED = "लॉग पथ आवश्यक है"
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@ -1532,7 +1530,9 @@ def __init__(self):
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self.STARTING_IMATRIX_GENERATION = "IMatrix उत्पादन शुरू हो रहा है"
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self.BACKEND_PATH_NOT_EXIST = "बैकएंड पथ मौजूद नहीं है: {0}"
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self.GENERATING_IMATRIX = "IMatrix उत्पन्न किया जा रहा है"
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self.ERROR_STARTING_IMATRIX_GENERATION = "IMatrix उत्पादन शुरू करने में त्रुटि: {0}"
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self.ERROR_STARTING_IMATRIX_GENERATION = (
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"IMatrix उत्पादन शुरू करने में त्रुटि: {0}"
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)
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self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix उत्पादन कार्य शुरू हुआ"
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self.ERROR_MESSAGE = "त्रुटि: {0}"
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self.TASK_ERROR = "कार्य त्रुटि: {0}"
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@ -1542,14 +1542,14 @@ def __init__(self):
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self.ALLOWS_REQUANTIZING = (
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"पहले से क्वांटाइज़ किए गए टेंसर को पुनः क्वांटाइज़ करने की अनुमति देता है"
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)
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self.LEAVE_OUTPUT_WEIGHT = "output.weight को अक्वांटाइज़ (या पुनः क्वांटाइज़) छोड़ देगा"
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self.DISABLE_K_QUANT_MIXTURES = (
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"k-quant मिश्रण को अक्षम करें और सभी टेंसर को एक ही प्रकार में क्वांटाइज़ करें"
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self.LEAVE_OUTPUT_WEIGHT = (
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"output.weight को अक्वांटाइज़ (या पुनः क्वांटाइज़) छोड़ देगा"
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)
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self.USE_DATA_AS_IMPORTANCE_MATRIX = (
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"क्वांट अनुकूलन के लिए फ़ाइल में डेटा को महत्व मैट्रिक्स के रूप में उपयोग करें"
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self.DISABLE_K_QUANT_MIXTURES = "k-quant मिश्रण को अक्षम करें और सभी टेंसर को एक ही प्रकार में क्वांटाइज़ करें"
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self.USE_DATA_AS_IMPORTANCE_MATRIX = "क्वांट अनुकूलन के लिए फ़ाइल में डेटा को महत्व मैट्रिक्स के रूप में उपयोग करें"
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self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
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"इन टेंसर के लिए महत्व मैट्रिक्स का उपयोग करें"
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)
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self.USE_IMPORTANCE_MATRIX_FOR_TENSORS = "इन टेंसर के लिए महत्व मैट्रिक्स का उपयोग करें"
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self.DONT_USE_IMPORTANCE_MATRIX_FOR_TENSORS = (
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"इन टेंसर के लिए महत्व मैट्रिक्स का उपयोग न करें"
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)
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@ -2006,9 +2006,7 @@ def __init__(self):
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self.RESTART = "再起動"
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self.DELETE = "削除"
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self.CONFIRM_DELETION = "このタスクを削除してもよろしいですか？"
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self.TASK_RUNNING_WARNING = (
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"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？"
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)
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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？"
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self.YES = "はい"
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self.NO = "いいえ"
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self.DOWNLOAD_COMPLETE = "ダウンロード完了"
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@ -2021,11 +2019,11 @@ def __init__(self):
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self.NO_MODEL_SELECTED = "モデルが選択されていません"
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self.REFRESH_RELEASES = "リリースを更新"
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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"
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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"
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self.CUDA_FILES_EXTRACTED = "CUDAファイルはに抽出されました"
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self.NO_SUITABLE_CUDA_BACKEND_EXTRACTION = (
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"抽出に適したCUDAバックエンドが見つかりませんでした"
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self.LLAMACPP_DOWNLOADED_EXTRACTED = (
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"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"
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)
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self.CUDA_FILES_EXTRACTED = "CUDAファイルはに抽出されました"
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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"
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self.ERROR_FETCHING_RELEASES = "リリースの取得中にエラーが発生しました: {0}"
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self.CONFIRM_DELETION_TITLE = "削除の確認"
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self.LOG_FOR = "{0}のログ"
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@ -2050,10 +2048,10 @@ def __init__(self):
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self.RESTARTING_TASK = "タスクを再起動しています: {0}"
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self.IN_PROGRESS = "処理中"
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self.DOWNLOAD_FINISHED_EXTRACTED_TO = "ダウンロードが完了しました。抽出先: {0}"
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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"
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self.NO_SUITABLE_CUDA_BACKEND_FOUND = (
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"抽出に適したCUDAバックエンドが見つかりませんでした"
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self.LLAMACPP_DOWNLOADED_AND_EXTRACTED = (
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"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"
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)
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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"
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self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = (
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"llama.cppバイナリがダウンロードされ、{0}に抽出されました"
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)
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@ -2103,42 +2101,24 @@ def __init__(self):
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self.STARTING_IMATRIX_GENERATION = "IMatrixの生成を開始しています"
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self.BACKEND_PATH_NOT_EXIST = "バックエンドパスが存在しません: {0}"
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self.GENERATING_IMATRIX = "IMatrixを生成しています"
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self.ERROR_STARTING_IMATRIX_GENERATION = (
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"IMatrixの生成を開始中にエラーが発生しました: {0}"
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)
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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}"
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self.IMATRIX_GENERATION_TASK_STARTED = "IMatrix生成タスクが開始されました"
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self.ERROR_MESSAGE = "エラー: {0}"
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self.TASK_ERROR = "タスクエラー: {0}"
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self.APPLICATION_CLOSING = "アプリケーションを終了しています"
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self.APPLICATION_CLOSED = "アプリケーションが終了しました"
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self.SELECT_QUANTIZATION_TYPE = "量子化タイプを選択してください"
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self.ALLOWS_REQUANTIZING = (
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"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"
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)
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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"
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self.LEAVE_OUTPUT_WEIGHT = "output.weightは（再）量子化されません"
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self.DISABLE_K_QUANT_MIXTURES = (
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"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"
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||||
)
|
||||
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"
|
||||
|
|
@ -2795,11 +2775,11 @@ 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"
|
||||
|
|
@ -2823,13 +2803,11 @@ 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."
|
||||
)
|
||||
|
|
@ -2866,14 +2844,10 @@ 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."
|
||||
|
|
@ -2883,9 +2857,7 @@ 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}"
|
||||
|
|
@ -2894,26 +2866,14 @@ 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."
|
||||
)
|
||||
|
|
@ -3868,7 +3828,9 @@ 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}"
|
||||
|
|
@ -3876,13 +3838,11 @@ 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"
|
||||
)
|
||||
|
|
@ -5986,9 +5946,7 @@ 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"
|
||||
|
|
@ -6045,18 +6003,14 @@ 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"
|
||||
|
|
|
|||
Loading…
Reference in New Issue