mirror of https://github.com/leafspark/AutoGGUF
feat: support multiple quantization types
This commit adds the ability to select and run multiple quantization types simultaneously. It includes: - Replacing the quantization type dropdown with a multi-select list - Updating preset saving and loading to handle multiple quantization types - Modifying the quantize_model function to process all selected types - fix formatting issue with previous commit - use error and in progress messages from localizations in QuantizationThread
This commit is contained in:
BuildTools
parent
e69237b7b9
commit
dc9a1c8971
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@ -125,10 +125,25 @@ def load_models(self):
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def quantize_model(self):
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"""
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Start the model quantization process.
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Initiates the quantization process for the selected model with the chosen quantization types.
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This method collects all the quantization settings, constructs the
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quantization command, and starts a new thread to run the quantization process.
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This function performs the following steps:
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1. Validates the input parameters and selected model.
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2. Retrieves the backend path and selected quantization types.
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3. For each selected quantization type:
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a. Constructs the output file name based on the quantization options.
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b. Builds the quantization command with all selected options.
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c. Creates a new thread for the quantization process.
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d. Sets up a task item in the GUI to display progress and status.
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e. Connects the thread signals to update the GUI and handle errors.
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f. Starts the quantization thread.
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The function handles various exceptions and displays error messages if any step fails.
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Raises:
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ValueError: If required inputs are missing or invalid.
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FileNotFoundError: If the input model file doesn't exist.
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Exception: For any other unexpected errors during the process.
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"""
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def update_model_info(self, model_info):
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339
src/AutoGGUF.py
339
src/AutoGGUF.py
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@ -144,48 +144,50 @@ def __init__(self):
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quant_options_widget = QWidget()
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quant_options_layout = QFormLayout()
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self.quant_type = QComboBox()
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self.quant_type.addItems(
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[
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"IQ2_XXS",
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"IQ2_XS",
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"IQ2_S",
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"IQ2_M",
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"IQ1_S",
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"IQ1_M",
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"Q2_K",
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"Q2_K_S",
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"IQ3_XXS",
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"IQ3_S",
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"IQ3_M",
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"Q3_K",
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"IQ3_XS",
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"Q3_K_S",
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"Q3_K_M",
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"Q3_K_L",
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"IQ4_NL",
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"IQ4_XS",
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"Q4_K",
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"Q4_K_S",
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"Q4_K_M",
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"Q5_K",
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"Q5_K_S",
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"Q5_K_M",
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"Q6_K",
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"Q8_0",
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"Q4_0",
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"Q4_1",
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"Q5_0",
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"Q5_1",
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"Q4_0_4_4",
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"Q4_0_4_8",
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"Q4_0_8_8",
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"BF16",
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"F16",
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"F32",
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"COPY",
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]
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)
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self.quant_type = QListWidget()
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self.quant_type.setMinimumHeight(100)
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self.quant_type.setMinimumWidth(150)
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self.quant_type.setSelectionMode(QListWidget.SelectionMode.MultiSelection)
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quant_types = [
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"IQ2_XXS",
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"IQ2_XS",
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"IQ2_S",
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"IQ2_M",
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"IQ1_S",
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"IQ1_M",
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"Q2_K",
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"Q2_K_S",
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"IQ3_XXS",
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"IQ3_S",
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"IQ3_M",
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"Q3_K",
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"IQ3_XS",
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"Q3_K_S",
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"Q3_K_M",
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"Q3_K_L",
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"IQ4_NL",
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"IQ4_XS",
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"Q4_K",
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"Q4_K_S",
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"Q4_K_M",
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"Q5_K",
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"Q5_K_S",
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"Q5_K_M",
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"Q6_K",
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"Q8_0",
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"Q4_0",
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"Q4_1",
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"Q5_0",
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"Q5_1",
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"Q4_0_4_4",
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"Q4_0_4_8",
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"Q4_0_8_8",
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"BF16",
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"F16",
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"F32",
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"COPY",
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]
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self.quant_type.addItems(quant_types)
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quant_options_layout.addRow(
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self.create_label(QUANTIZATION_TYPE, SELECT_QUANTIZATION_TYPE),
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self.quant_type,
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@ -565,6 +567,9 @@ def __init__(self):
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# Initialize threads
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self.quant_threads = []
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# Load models
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self.load_models()
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self.logger.info(AUTOGGUF_INITIALIZATION_COMPLETE)
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def refresh_backends(self):
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@ -598,7 +603,7 @@ def update_base_model_visibility(self, index):
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def save_preset(self):
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self.logger.info(SAVING_PRESET)
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preset = {
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"quant_type": self.quant_type.currentText(),
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"quant_types": [item.text() for item in self.quant_type.selectedItems()],
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"allow_requantize": self.allow_requantize.isChecked(),
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"leave_output_tensor": self.leave_output_tensor.isChecked(),
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"pure": self.pure.isChecked(),
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@ -633,7 +638,11 @@ def load_preset(self):
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with open(file_name, "r") as f:
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preset = json.load(f)
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self.quant_type.setCurrentText(preset.get("quant_type", ""))
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self.quant_type.clearSelection()
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for quant_type in preset.get("quant_types", []):
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items = self.quant_type.findItems(quant_type, Qt.MatchExactly)
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if items:
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items[0].setSelected(True)
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self.allow_requantize.setChecked(preset.get("allow_requantize", False))
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self.leave_output_tensor.setChecked(
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preset.get("leave_output_tensor", False)
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@ -1364,126 +1373,154 @@ def quantize_model(self):
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backend_path = self.backend_combo.currentData()
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if not backend_path:
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raise ValueError(NO_BACKEND_SELECTED)
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quant_type = self.quant_type.currentText()
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selected_quant_types = [
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item.text() for item in self.quant_type.selectedItems()
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]
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if not selected_quant_types:
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raise ValueError(NO_QUANTIZATION_TYPE_SELECTED)
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input_path = os.path.join(self.models_input.text(), model_file)
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# Start building the output name
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output_name_parts = [
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os.path.splitext(model_name)[0],
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"converted",
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quant_type,
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]
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# Check for output tensor options
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if (
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self.use_output_tensor_type.isChecked()
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or self.leave_output_tensor.isChecked()
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):
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output_tensor_part = "o"
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if self.use_output_tensor_type.isChecked():
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output_tensor_part += "." + self.output_tensor_type.currentText()
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output_name_parts.append(output_tensor_part)
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# Check for embedding tensor options
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if self.use_token_embedding_type.isChecked():
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embd_tensor_part = "t." + self.token_embedding_type.currentText()
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output_name_parts.append(embd_tensor_part)
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# Check for pure option
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if self.pure.isChecked():
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output_name_parts.append("pure")
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# Check for requantize option
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if self.allow_requantize.isChecked():
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output_name_parts.append("rq")
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# Check for KV override
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if any(entry.get_override_string() for entry in self.kv_override_entries):
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output_name_parts.append("kv")
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# Join all parts with underscores and add .gguf extension
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output_name = "_".join(output_name_parts) + ".gguf"
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output_path = os.path.join(self.output_input.text(), output_name)
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if not os.path.exists(input_path):
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raise FileNotFoundError(INPUT_FILE_NOT_EXIST.format(input_path))
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command = [os.path.join(backend_path, "llama-quantize")]
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tasks = [] # List to keep track of all tasks
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if self.allow_requantize.isChecked():
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command.append("--allow-requantize")
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if self.leave_output_tensor.isChecked():
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command.append("--leave-output-tensor")
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if self.pure.isChecked():
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command.append("--pure")
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if self.imatrix.text():
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command.extend(["--imatrix", self.imatrix.text()])
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if self.include_weights.text():
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command.extend(["--include-weights", self.include_weights.text()])
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if self.exclude_weights.text():
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command.extend(["--exclude-weights", self.exclude_weights.text()])
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if self.use_output_tensor_type.isChecked():
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command.extend(
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["--output-tensor-type", self.output_tensor_type.currentText()]
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)
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if self.use_token_embedding_type.isChecked():
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command.extend(
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["--token-embedding-type", self.token_embedding_type.currentText()]
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)
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if self.keep_split.isChecked():
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command.append("--keep-split")
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if self.kv_override_entries:
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for entry in self.kv_override_entries:
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override_string = entry.get_override_string(
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model_name=model_name,
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quant_type=quant_type,
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output_path=output_path,
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for quant_type in selected_quant_types:
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# Start building the output name
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output_name_parts = [
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os.path.splitext(model_name)[0],
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"converted",
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quant_type,
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]
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# Check for output tensor options
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if (
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self.use_output_tensor_type.isChecked()
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or self.leave_output_tensor.isChecked()
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):
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output_tensor_part = "o"
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if self.use_output_tensor_type.isChecked():
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output_tensor_part += (
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"." + self.output_tensor_type.currentText()
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)
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output_name_parts.append(output_tensor_part)
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# Check for embedding tensor options
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if self.use_token_embedding_type.isChecked():
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embd_tensor_part = "t." + self.token_embedding_type.currentText()
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output_name_parts.append(embd_tensor_part)
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# Check for pure option
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if self.pure.isChecked():
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output_name_parts.append("pure")
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# Check for requantize option
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if self.allow_requantize.isChecked():
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output_name_parts.append("rq")
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# Check for KV override
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if any(
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entry.get_override_string() for entry in self.kv_override_entries
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):
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output_name_parts.append("kv")
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# Join all parts with underscores and add .gguf extension
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output_name = "_".join(output_name_parts) + ".gguf"
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output_path = os.path.join(self.output_input.text(), output_name)
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command = [os.path.join(backend_path, "llama-quantize")]
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if self.allow_requantize.isChecked():
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command.append("--allow-requantize")
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if self.leave_output_tensor.isChecked():
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command.append("--leave-output-tensor")
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if self.pure.isChecked():
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command.append("--pure")
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if self.imatrix.text():
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command.extend(["--imatrix", self.imatrix.text()])
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if self.include_weights.text():
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command.extend(["--include-weights", self.include_weights.text()])
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if self.exclude_weights.text():
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command.extend(["--exclude-weights", self.exclude_weights.text()])
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if self.use_output_tensor_type.isChecked():
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command.extend(
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["--output-tensor-type", self.output_tensor_type.currentText()]
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)
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if override_string:
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command.extend(["--override-kv", override_string])
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if self.use_token_embedding_type.isChecked():
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command.extend(
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[
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"--token-embedding-type",
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self.token_embedding_type.currentText(),
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]
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)
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if self.keep_split.isChecked():
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command.append("--keep-split")
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if self.kv_override_entries:
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for entry in self.kv_override_entries:
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override_string = entry.get_override_string(
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model_name=model_name,
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quant_type=quant_type,
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output_path=output_path,
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)
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if override_string:
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command.extend(["--override-kv", override_string])
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command.extend([input_path, output_path, quant_type])
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command.extend([input_path, output_path, quant_type])
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# Add extra arguments
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if self.extra_arguments.text():
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command.extend(self.extra_arguments.text().split())
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# Add extra arguments
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if self.extra_arguments.text():
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command.extend(self.extra_arguments.text().split())
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logs_path = self.logs_input.text()
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ensure_directory(logs_path)
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logs_path = self.logs_input.text()
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ensure_directory(logs_path)
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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log_file = os.path.join(
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logs_path, f"{model_name}_{timestamp}_{quant_type}.log"
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)
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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log_file = os.path.join(
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logs_path, f"{model_name}_{timestamp}_{quant_type}.log"
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)
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# Log quant command
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command_str = " ".join(command)
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self.logger.info(f"{QUANTIZATION_COMMAND}: {command_str}")
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# Log quant command
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command_str = " ".join(command)
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self.logger.info(f"{QUANTIZATION_COMMAND}: {command_str}")
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thread = QuantizationThread(command, backend_path, log_file)
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self.quant_threads.append(thread)
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thread = QuantizationThread(command, backend_path, log_file)
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self.quant_threads.append(thread)
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task_item = TaskListItem(
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QUANTIZING_MODEL_TO.format(model_name, quant_type), log_file
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)
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list_item = QListWidgetItem(self.task_list)
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list_item.setSizeHint(task_item.sizeHint())
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self.task_list.addItem(list_item)
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self.task_list.setItemWidget(list_item, task_item)
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task_item = TaskListItem(
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QUANTIZING_MODEL_TO.format(model_name, quant_type), log_file
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)
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list_item = QListWidgetItem(self.task_list)
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list_item.setSizeHint(task_item.sizeHint())
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self.task_list.addItem(list_item)
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self.task_list.setItemWidget(list_item, task_item)
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tasks.append(
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(thread, task_item)
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) # Add the thread and task_item to our list
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# Connect the output signal to the new progress parsing function
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thread.output_signal.connect(
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lambda line, ti=task_item: self.parse_progress(line, ti)
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)
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thread.status_signal.connect(task_item.update_status)
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thread.finished_signal.connect(
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lambda t=thread, ti=task_item: self.task_finished(t, ti)
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)
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thread.error_signal.connect(
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lambda err, ti=task_item: self.handle_error(err, ti)
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)
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thread.model_info_signal.connect(self.update_model_info)
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# Start all threads after setting them up
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for thread, _ in tasks:
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thread.start()
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self.logger.info(QUANTIZATION_TASK_STARTED.format(model_name))
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# Connect the output signal to the new progress parsing function
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thread.output_signal.connect(
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lambda line: self.parse_progress(line, task_item)
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)
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thread.status_signal.connect(task_item.update_status)
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thread.finished_signal.connect(lambda: self.task_finished(thread))
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thread.error_signal.connect(lambda err: self.handle_error(err, task_item))
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thread.model_info_signal.connect(self.update_model_info)
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thread.start()
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self.logger.info(QUANTIZATION_TASK_STARTED.format(model_name))
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except ValueError as e:
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self.show_error(str(e))
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except FileNotFoundError as e:
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self.show_error(str(e))
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except Exception as e:
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self.show_error(ERROR_STARTING_QUANTIZATION.format(str(e)))
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@ -1501,10 +1538,11 @@ def parse_progress(self, line, task_item):
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progress = int((current / total) * 100)
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task_item.update_progress(progress)
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def task_finished(self, thread):
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def task_finished(self, thread, task_item):
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self.logger.info(TASK_FINISHED.format(thread.log_file))
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if thread in self.quant_threads:
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self.quant_threads.remove(thread)
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task_item.update_status(COMPLETED)
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def show_task_details(self, item):
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self.logger.debug(SHOWING_TASK_DETAILS_FOR.format(item.text()))
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@ -1630,11 +1668,10 @@ def show_error(self, message):
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self.logger.error(ERROR_MESSAGE.format(message))
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QMessageBox.critical(self, ERROR, message)
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def handle_error(self, error_message, task_item, task_exists=True):
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def handle_error(self, error_message, task_item):
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self.logger.error(TASK_ERROR.format(error_message))
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self.show_error(error_message)
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if task_exists:
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task_item.set_error()
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task_item.update_status(ERROR)
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def closeEvent(self, event: QCloseEvent):
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self.logger.info(APPLICATION_CLOSING)
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@ -14,6 +14,7 @@
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import traceback
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from datetime import datetime
|
||||
from imports_and_globals import open_file_safe
|
||||
from localizations import *
|
||||
|
||||
|
||||
class QuantizationThread(QThread):
|
||||
|
|
@ -49,13 +50,13 @@ def run(self):
|
|||
self.output_signal.emit(line)
|
||||
log.write(line + "\n")
|
||||
log.flush()
|
||||
self.status_signal.emit("In Progress")
|
||||
self.status_signal.emit(IN_PROGRESS)
|
||||
self.parse_model_info(line)
|
||||
|
||||
# Wait for process to complete
|
||||
self.process.wait()
|
||||
if self.process.returncode == 0:
|
||||
self.status_signal.emit("Completed")
|
||||
self.status_signal.emit(COMPLETED)
|
||||
self.model_info_signal.emit(self.model_info)
|
||||
else:
|
||||
self.error_signal.emit(
|
||||
|
|
|
|||
|
|
@ -230,6 +230,7 @@ def __init__(self):
|
|||
self.IMATRIX_GENERATION_COMMAND = ""
|
||||
self.LORA_CONVERSION_COMMAND = ""
|
||||
self.LORA_EXPORT_COMMAND = ""
|
||||
self.NO_QUANTIZATION_TYPE_SELECTED = ""
|
||||
|
||||
|
||||
class _English(_Localization):
|
||||
|
|
@ -502,6 +503,7 @@ def __init__(self):
|
|||
self.IMATRIX_GENERATION_COMMAND = "IMatrix generation command"
|
||||
self.LORA_CONVERSION_COMMAND = "LoRA conversion command"
|
||||
self.LORA_EXPORT_COMMAND = "LoRA export command"
|
||||
self.NO_QUANTIZATION_TYPE_SELECTED = "No quantization type selected. Please select at least one quantization type."
|
||||
|
||||
|
||||
class _French:
|
||||
|
|
@ -857,7 +859,9 @@ def __init__(self):
|
|||
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.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.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}"
|
||||
|
|
@ -888,7 +892,9 @@ def __init__(self):
|
|||
"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.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = "llama.cpp二进制文件已下载并提取到{0}"
|
||||
self.LLAMACPP_BINARY_DOWNLOADED_AND_EXTRACTED = (
|
||||
"llama.cpp二进制文件已下载并提取到{0}"
|
||||
)
|
||||
self.REFRESHING_LLAMACPP_RELEASES = "刷新llama.cpp发布版本"
|
||||
self.UPDATING_ASSET_LIST = "更新资源列表"
|
||||
self.UPDATING_CUDA_OPTIONS = "更新CUDA选项"
|
||||
|
|
@ -952,7 +958,9 @@ def __init__(self):
|
|||
self.USE_THIS_TYPE_FOR_OUTPUT_WEIGHT = "对output.weight张量使用此类型"
|
||||
self.TOKEN_EMBEDDING_TYPE = "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.WILL_GENERATE_QUANTIZED_MODEL_IN_SAME_SHARDS = "将在与输入相同的分片中生成量化模型"
|
||||
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"
|
||||
)
|
||||
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"
|
||||
|
|
@ -1400,7 +1408,9 @@ 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"
|
||||
|
|
@ -1429,7 +1439,9 @@ 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"
|
||||
)
|
||||
|
|
@ -1451,25 +1463,17 @@ 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"
|
||||
|
|
@ -1496,9 +1500,7 @@ 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}"
|
||||
|
|
@ -1508,14 +1510,14 @@ 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"
|
||||
)
|
||||
|
|
@ -1972,7 +1974,9 @@ 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"
|
||||
|
|
@ -1985,11 +1989,11 @@ 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"
|
||||
|
|
@ -2014,10 +2018,10 @@ 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"
|
||||
)
|
||||
|
|
@ -2067,24 +2071,42 @@ 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"
|
||||
|
|
@ -2741,11 +2763,11 @@ 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"
|
||||
|
|
@ -2769,11 +2791,13 @@ 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."
|
||||
)
|
||||
|
|
@ -2810,10 +2834,14 @@ 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."
|
||||
|
|
@ -2823,7 +2851,9 @@ 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}"
|
||||
|
|
@ -2832,14 +2862,26 @@ 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."
|
||||
)
|
||||
|
|
@ -3794,9 +3836,7 @@ 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}"
|
||||
|
|
@ -3804,11 +3844,13 @@ 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"
|
||||
)
|
||||
|
|
@ -5912,7 +5954,9 @@ 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"
|
||||
|
|
@ -5969,14 +6013,18 @@ 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"
|
||||
|
|
|
|||
Loading…
Reference in New Issue