Large model-based standard processing method
By ensuring data quality and diversity in the standard processing methods of large models, selecting the appropriate large model architecture, and performing hyperparameter tuning and model compression, the time-consuming and expensive data acquisition problem is solved, and the fairness and accuracy of the large model results are achieved.
Patent Information
- Application Number
- CN202510035694.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-09
- Publication Date
- 2025-05-27
AI Technical Summary
Existing standardized processing methods based on large models require a large amount of high-quality data when training data, and data acquisition is time-consuming and expensive, which may lead to bias in the model learning and produce unfair or inaccurate results.
By analyzing the scope and resources of the big model, ensuring the quality and diversity of data, data annotation and segmentation, selecting the appropriate big model architecture, and using grid search or random search for hyperparameter tuning, model training and compression, ensuring the generalization ability and performance of the model.
It effectively ensures the speed and low cost of data acquisition, ensures the fairness and accuracy of the results of large models, and improves the generalization ability and inference speed of the model.
Smart Images
Figure CN120045936A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of standard processing, and specifically provides a standard processing method based on a large model. Background Art
[0002] The development of large model technology has triggered a software engineering revolution, subverting the product development paradigm. Therefore, more and more enterprises are applying large models on a large scale in various business scenarios. With people paying more and more attention to their privacy data and the wide application of large models, business standard management based on large models is an important application field;
[0003] However, the existing standard processing methods based on large models still have the following technical problems when in use:
[0004] The large models in the existing processing methods require a large amount of high-quality data for training, and obtaining and annotating these data can be very time-consuming and expensive. If the training data is biased or unbalanced, the large model may learn these biases, resulting in unfair or inaccurate results;
[0005] Therefore, a standard processing method based on a large model is proposed to solve the above-mentioned problems. Summary of the Invention
[0006] The purpose of the present invention is to provide a standard processing method based on a large model to solve the problems raised in the above background art.
[0007] To achieve the above purpose, the present invention provides the following technical solution: A standard processing method based on a large model, and the specific step process of this standard processing method is as follows:
[0008] Step 1: Analyze the large model target to be used, define the scope of this large model, and evaluate the available computing resources, data resources, and time budget;
[0009] Step 2: Collect the relevant data required to be used in the large model, and ensure the quality and diversity of the data. After the data collection is completed, delete the internal duplicate data, process the missing values and correct the errors, standardize the data format, ensure the consistency of the data, annotate this data, and segment the data;
[0010] Step 3: Select a suitable large model architecture according to the task type, consider the scale and complexity of the model, ensure that the computing resources can support it, perform word segmentation, encoding, padding, and truncation on the text data, perform scaling and cropping on the image data, and perform sampling and feature extraction on the audio data;
[0011] Step 4: Train the model, set hyperparameters such as learning rate, batch size, number of training epochs, etc., and use grid search or random search for hyperparameter tuning. Use a deep learning framework to train the model and monitor the loss function and performance metrics during training. Evaluate the model performance on the validation set, and adjust the hyperparameters or model structure according to the results.
[0012] Step 5: Evaluate the final performance of the model on the test set to ensure that the model has good generalization ability. Calculate and record the key performance metrics, analyze the error cases of the model on the test set, identify the weaknesses of the model, and make targeted improvements based on the error analysis results.
[0013] Step 6: Compress the model to reduce its size and computational complexity, improve the inference speed and reduce resource consumption while maintaining the model performance. Use hardware acceleration for inference, optimize the inference code, and reduce latency and memory occupancy.
[0014] Step 7: Deploy the trained model, monitor the performance and health status of the model in real time to ensure the stable operation of the system. Use a logging and alerting system to detect and handle problems in a timely manner. Regularly collect new data, retrain and update the model, and optimize according to user feedback and business requirements.
[0015] Preferably, when analyzing the large model target, it is necessary to clarify the specific application field and expected function of the model, determine the type of problems that the model needs to solve, and select appropriate performance metrics according to the application scenario of the model.
[0016] Preferably, when collecting the relevant data needed in the large model, it is necessary to ensure that the collected data can fully represent the actual distribution in the target application scenario, avoid biases that may lead to insufficient model training or generate biases. The data needs to be accurate, complete, and consistent, ensure that the dataset contains sufficient diversity and balance between categories, and implement strict security measures during the entire data collection process to prevent data leakage or unauthorized access.
[0017] Preferably, when choosing the large model architecture, it is necessary to select different model architectures according to different task types, analyze the characteristics of the input data, clarify the expectations for aspects such as model speed, accuracy, and resource consumption. If high real-time requirements exist, architectures with fast inference speed should be given priority.
[0018] Preferably, when training the model, it is necessary to ensure the quality, diversity, and representativeness of the training data, require a large amount of computing resources, select appropriate hardware configurations, and consider distributed training to accelerate the training process. Ensure that there is sufficient storage space to save model parameters, logs, and other related files, and conduct systematic search and tuning of the hyperparameters in the model architecture.
[0019] Preferably, during the process of compressing the model, an appropriate compression method needs to be selected. The compression methods include quantization, pruning, and knowledge distillation. Quantization is to convert floating-point weights into low-precision integer representations. Pruning is to remove unimportant connections or neurons in the network to simplify the model structure. Knowledge distillation is to use a larger model to guide the training of a smaller model so that the latter can learn the key features of the former.
[0020] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0021] After collecting the data in the large model, this application trains the model, uses grid search or random search for hyperparameter tuning, uses a deep learning framework for model training, and monitors the loss function and performance metrics during the training process. It can not only effectively ensure the speed of data acquisition, but also ensure that the data acquisition method is inexpensive, making the results of the large model more fair. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Figure 1 It is a schematic structural diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.
[0024] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.
[0025] Embodiment:
[0026] Please refer to Figure 1 , the present invention provides a technical solution:
[0027] A standard processing method based on a large model. The specific step flow of this standard processing method is as follows:
[0028] Step 1: Analyze the large model target to be used, define the scope of the large model, and evaluate the available computing resources, data resources, and time budget;
[0029] Step 2: Collect relevant data to be used within the large model and ensure the quality and diversity of the data. After data collection is completed, delete duplicate data within, handle missing values, correct errors, standardize the data format to ensure data consistency, annotate this data, segment the data, and divide the data into a training set, a validation set, and a test set, usually in the proportions of 70%, 15%, and 15%;
[0030] Step 3: Select an appropriate large model architecture according to the task type, consider the scale and complexity of the model, ensure that the computing resources can support it, tokenize, encode, pad, and truncate the text data, scale and crop the image data, and sample and extract features from the audio data;
[0031] Step 4: Train the model, set hyperparameters such as the learning rate, batch size, and number of training epochs, and use grid search or random search for hyperparameter tuning. Use a deep learning framework to train the model and monitor the loss function and performance metrics during the training process. Evaluate the model performance on the validation set and adjust the hyperparameters or model structure according to the results;
[0032] Step 5: Evaluate the final performance of the model on the test set, ensure that the model has good generalization ability, calculate and record key performance metrics such as accuracy and recall, analyze the error cases of the model on the test set, identify the weaknesses of the model, and make targeted improvements based on the error analysis results;
[0033] Step 6: Compress the model to reduce the size and computational amount of the model, improve the inference speed and reduce resource consumption while maintaining the model performance, use hardware acceleration for inference, optimize the inference code, and reduce latency and memory occupancy;
[0034] Step 7: Deploy the trained model, monitor the performance and health status of the model in real time to ensure the stable operation of the system, use a logging and alerting system to detect and handle problems in a timely manner, regularly collect new data, retrain and update the model, and optimize according to user feedback and business requirements.
[0035] When analyzing the large model objectives, it is necessary to clarify the specific application field and expected functions of the model, determine the type of problems the model needs to solve, and select appropriate performance metrics according to the application scenario of the model.
[0036] When collecting relevant data to be used within a large model, it is necessary to ensure that the collected data can fully represent the actual distribution in the target application scenario, avoiding biases that may lead to insufficient model training or the generation of biases. The data needs to be accurate, complete, and consistent, ensuring that the dataset contains sufficient diversity and balance among categories. Implement strict security measures throughout the data collection process to prevent data leakage or unauthorized access.
[0037] When selecting a large model architecture, it is necessary to choose different model architectures according to different task types, analyze the characteristics of the input data, and clarify the expectations regarding aspects such as model speed, accuracy, and resource consumption. If high real-time requirements exist, architectures with fast inference speeds should be given priority.
[0038] When training the model, it is necessary to ensure the quality, diversity, and representativeness of the training data, which requires a large amount of computing resources. Select appropriate hardware configurations and consider distributed training to accelerate the training process. Ensure there is sufficient storage space to save model parameters, logs, and other relevant files, and conduct systematic search and tuning of the hyperparameters in the model architecture.
[0039] During the process of compressing the model, it is necessary to select appropriate compression methods, including quantization, pruning, and knowledge distillation. Quantization is the conversion of floating-point weights to low-precision integer representations. Pruning is the removal of unimportant connections or neurons in the network to simplify the model structure. Knowledge distillation is the use of a larger model to guide the training of a smaller model so that the latter can learn the key features of the former.
[0040] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed within the present invention, and any reference signs in the claims should not be regarded as limiting the claims involved.
[0041] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A standard processing method based on a large model, characterized in that: The specific steps of this standard processing method are as follows: Step 1: Analyze the target of the large model to be used, define the scope of the large model, and evaluate the available computing resources, data resources, and time budget; Step 2: Collect relevant data needed in the large model and ensure the quality and diversity of the data. After data collection is completed, delete internal duplicate data, handle missing values and correct errors, standardize data formats, ensure data consistency, label the data, and segment the data. Step 3: Select the appropriate large model architecture according to the task type, consider the scale and complexity of the model, ensure that the computing resources can support it, segment, encode, fill, and truncate the text data, scale and crop the image data, and sample and extract features from the audio data; Step 4: Train the model, set hyperparameters such as learning rate, batch size, and number of training rounds, and use grid search or random search to tune hyperparameters. Use a deep learning framework to train the model and monitor the loss function and performance indicators during training. Evaluate model performance on the validation set and adjust hyperparameters or model structure based on the results. Step 5: Evaluate the final performance of the model on the test set to ensure that the model has good generalization ability, calculate and record key performance indicators, analyze the error cases of the model on the test set, find out the weaknesses of the model, and make targeted improvements based on the error analysis results; Step 6: Compress the model to reduce the size and computational complexity of the model, improve the inference speed and reduce resource consumption while maintaining the model performance, use hardware acceleration for inference, optimize the inference code, and reduce latency and memory usage; Step 7: Deploy the trained model, monitor the performance and health of the model in real time, ensure the stable operation of the system, use the logging and alarm system to promptly discover and handle problems, regularly collect new data, retrain and update the model, and optimize according to user feedback and business needs.
2. A large model-based standard processing method according to claim 1, characterized in that: When analyzing the objectives of a large model, it is necessary to clarify the specific application areas and expected functions of the model, determine the types of problems that the model needs to solve, and select appropriate performance metrics based on the model's application scenarios.
3. A large model-based standard processing method according to claim 1, characterized in that: When collecting relevant data needed to be used in a large model, it is necessary to ensure that the collected data should be able to fully represent the actual distribution in the target application scenario to avoid deviations that lead to insufficient model training or bias. The data needs to be accurate, complete and consistent, ensuring that the data set contains sufficient diversity and balance between categories. Strict security measures should be implemented throughout the data collection process to prevent data leakage or unauthorized access.
4. The large model-based standard processing method according to claim 1, characterized in that: When choosing a large model architecture, you need to select different model architectures according to different task types, analyze the characteristics of the input data, and clarify your expectations for model speed, accuracy, resource consumption, etc. If real-time requirements are high, you need to give priority to architectures with fast inference speeds.
5. The large model-based standard processing method according to claim 1, characterized in that: When training the model, it is necessary to ensure the quality, diversity, and representativeness of the training data. A large amount of computing resources is required. Appropriate hardware configuration should be selected. Distributed training should be considered to speed up the training process. Sufficient storage space should be ensured to save model parameters, logs, and other related files. The hyperparameters in the model architecture should be systematically searched and tuned.
6. A large model-based standard processing method according to claim 1, characterized in that: In the process of compressing the model, it is necessary to select a suitable compression method, which includes quantization, pruning and knowledge distillation. Quantization is to convert floating-point weights into low-precision integer representations. Pruning is to remove unimportant connections or neurons in the network to simplify the model structure. Knowledge distillation is to use a larger model to guide the training of a smaller model so that the latter can learn the key features of the former.