Fine adjustment and deployment method and system for domain-specific large model based on adaptive optimization

Abstract

The invention discloses a field specialized large model fine tuning and deployment method and system based on adaptive optimization, belongs to the technical field of artificial intelligence and machine learning, and aims to solve the technical problem of how to improve the rapid adaptation capability of a large model on small-scale field data. In order to meet the requirements of special fields of law, medical treatment, finance and the like on professional terms and complex contexts, the technical scheme adopted by the invention comprises the following steps of: data processing and sample generation: performing cleaning, feature extraction and small sample expansion on field data, and generating a high-quality training sample through a field feature guide mechanism; small-sample fine tuning and migration: realizing rapid field adaptation of a large model through a small-sample learning technology and dynamic Prompt optimization, and reducing dependence on large-scale annotation data in combination with cross-field migration learning; compressing and optimizing the model; performing automatic deployment and collaborative reasoning; and monitoring and adaptive optimization are carried out.

Description

Technical Field

[0001] The present invention relates to the technical fields of artificial intelligence and machine learning, and in particular to a method and system for fine-tuning and deploying a domain-specific large model based on adaptive optimization. Background Art

[0002] With the rapid development of artificial intelligence (AI) technology, large-scale pre-trained models (such as GPT, BERT, etc.) have been widely applied in fields such as natural language processing (NLP) and computer vision (CV). They have demonstrated powerful generality and performance in tasks such as text generation, machine translation, and question-answering systems. However, large models still face significant challenges in practical applications:

[0003] ①Insufficient domain adaptation ability: Pre-trained models mainly rely on large-scale general data for training. However, in specific domains (such as law, medicine, finance), tasks often involve professional terms, complex contexts, or highly customized requirements. This makes it difficult for the model to effectively adapt to small-sample domain data, and the generated results may lack domain characteristics and practical significance. Commonly used fine-tuning methods in the prior art usually rely on large-scale labeled data, and under small-sample conditions, the effect of fine-tuning is often limited.

[0004] ②High model computing resource consumption: The parameter scale of large models is extremely large (such as in the billions or even hundreds of billions), and the demand for hardware resources is extremely high, which severely restricts their practical deployment and application in resource-constrained environments. For example, in edge devices or local computing scenarios, the running speed and memory occupancy of existing models are difficult to meet the requirements of real-time and economy.

[0005] ③High deployment complexity: The deployment process of large models usually requires complex engineering operations, including model optimization, compression, migration, adaptation, etc., and needs to be customized for different hardware environments. This not only increases the deployment cost but also significantly raises the technical threshold, restricting the widespread application of large models.

[0006] In recent years, in response to the above problems, some technical directions have gradually proposed optimization solutions. For example, through few-shot learning and prompt engineering, the model can be quickly adapted to small-data scenarios; through methods such as model pruning, quantization, and knowledge distillation, the scale and performance of large models can be optimized; and through cloud-edge collaboration mechanisms, the flexibility of model deployment can be improved. However, most existing solutions are limited to a single link and lack a complete solution from data processing, model optimization to deployment and application. In particular, the application effect in domain-specific requirements and resource-constrained environments is still limited.

[0007] Therefore, how to improve the rapid adaptation capability of large models on small-scale domain data and meet the requirements of specific fields such as law, medical care, and finance for professional terminology and complex contexts is a technical problem that needs to be solved urgently. Summary of the invention

[0008] The technical task of the present invention is to provide a method and system for fine-tuning and deploying a domain-specific large model based on adaptive optimization to solve the problem of how to improve the rapid adaptation capability of the large model on small-scale domain data and meet the requirements of specific fields such as law, medicine, and finance for professional terminology and complex contexts.

[0009] The technical task of the present invention is achieved in the following way: a method for fine-tuning and deploying a domain-specific large model based on adaptive optimization, the method is as follows:

[0010] Data processing and sample generation: clean domain data, extract features, and expand small samples, and generate high-quality training samples through domain feature guidance mechanisms;

[0011] Few-sample fine-tuning and transfer: The fast domain adaptation of large models is achieved through few-sample learning technology and dynamic prompt optimization, and cross-domain transfer learning is combined to reduce the dependence on large-scale labeled data;

[0012] Model compression and optimization: Use task-aware pruning, multi-scale quantization, and knowledge distillation techniques to optimize large models, reducing their size and computing resource requirements;

[0013] Automated deployment and collaborative reasoning: Supports one-click deployment of large models from optimization to cloud-edge-end environments, and optimizes reasoning paths based on domain characteristics to meet real-time requirements;

[0014] Monitoring and adaptive optimization: Real-time monitoring of large model performance, dynamic adjustment of operating parameters based on feedback, to ensure the stability and efficiency of the large model.

[0015] As a preference, data processing and sample generation are specifically as follows:

[0016] Ensure the quality and consistency of input data through data cleaning and standardization, as follows:

[0017] In the legal field, we clean up unnecessary punctuation and chaotically formatted noise information in legal documents, and segment, number and parse the text into clauses;

[0018] In the medical field, image data is denoised and normalized, and key fields in the diagnosis report are extracted and stored in a unified format;

[0019] The cleaned text and image data are further decomposed into structured information, that is, the scope of application of legal provisions or the lesion area of ​​medical images are extracted to ensure that the input data meets the training requirements of the large model;

[0020] By analyzing domain rules and features, the few-sample dataset is automatically expanded as follows:

[0021] In the legal field, case samples of different applicable scenarios are generated through stripe correlation analysis or the time, region and background conditions of the case are adjusted to expand sample diversity;

[0022] In the medical field, image enhancement techniques such as rotation, mirroring or noise injection are combined to generate more variant samples, while semantic transformations such as synonym replacement or word order adjustment of diagnostic reports are used to enrich text samples.

[0023] Improve diversity and representation through dynamic recombination and feature selection as follows:

[0024] In text processing, key features are extracted through TF-IDF and named entity recognition (NER) to construct a high-weight sample set;

[0025] In image processing, convolutional neural networks are used to enhance the extraction of regional features, with a focus on training key lesions or specific areas.

[0026] As a preferred method, the few-sample fine-tuning and migration are as follows:

[0027] Using few-sample learning technology, dynamic prompt optimization is used to enhance the large model's ability to understand domain-specific tasks: dynamic prompt templates are generated according to domain tasks, as follows:

[0028] In the legal field, the prompt template embeds contextual information of the article number and case background, guiding the big model to generate answers that are more in line with legal logic;

[0029] In the medical field, the prompt template adds auxiliary information such as patient examination reports and historical symptoms to improve the adaptability of the large model to diagnostic tasks;

[0030] At the same time, the Prompt template can dynamically adjust the content weight, specifically: increase the weight of core keywords in long text tasks to ensure that the large model focuses on key details;

[0031] Leverage the general features of the source domain to support few-shot tasks in the target domain: Freeze the layers in the pre-trained large model that have a correlation with the target task lower than the set threshold, and only adjust the parameters for the highly relevant layers, thereby reducing the dependence on large-scale labeled data in the target domain; specifically: When migrating from the financial contract summary task to the legal contract analysis task, extract the general ability of text summarization and only optimize the legal clause parsing part;

[0032] Combine an adaptive parameter adjustment mechanism to dynamically adjust the training strategy according to task complexity: By analyzing task length, keyword distribution, and semantic complexity features, calculate the parameter adjustment weight in real time; specifically as follows:

[0033] For complex tasks, the large model increases the learning rate step size, extends the context window, and enhances the ability to capture multi-dimensional information;

[0034] For simple tasks, compress the training step size, quickly complete the optimization, and avoid resource waste.

[0035] Preferably, model compression and optimization are as follows:

[0036] Adopt task-aware pruning technology. By dynamically evaluating the importance of parameters in the large model, prune the redundant parts and only retain the key parameters that have a greater impact on task performance; among them, the importance scoring formula for pruning is as follows:

[0037]

[0038] Among them, I(w i ) represents the comprehensive importance score of the parameter; represents the loss function of the target task; Var(w i ) is the variance of the parameter distribution, reflecting the dynamic characteristics of the parameter in the model; CosSim(w i , w j ) is the cosine similarity between the parameter w i and other parameters w j ; α and β are adjustable weights to balance the influence of gradient contribution, distribution dynamics, and redundancy similarity; through comprehensive calculation of gradients, parameter distributions, and redundant information, the model can dynamically remove redundant parameters while maintaining the critical path for task performance;

[0039] Optimize the weight representation using multi-scale quantization technology. Adopt different quantization precisions for different parts of the model to compress the model to the greatest extent; among them, the quantization process is achieved by minimizing the total quantization error, and the optimization objective is:

[0040]

[0041] Among them, w iis the weight in the model; Q(w i , b) is the quantization function; b represents the quantization bit width (such as 4-bit or 8-bit); Equant is the quantization error function; is the regularization term, γ is the weight coefficient, which is used to avoid the performance degradation caused by over-compression of the model; by adjusting b and γ, the quantization bit width is dynamically allocated according to the task requirements to achieve a balance between high performance and high compression ratio;

[0042] The fine-tuned large model is used as the teacher model through the knowledge distillation technology, and the soft label is generated to guide the small model to learn the key features; among them, the distillation process combines the task loss and the distillation loss, and the joint optimization objective formula is as follows:

[0043]

[0044] Among them, L task represents the loss of the target task; T represents the distillation temperature, which controls the smoothness of the output distribution of the teacher model; KL(p t , p s ) represents the Kullback-Leibler divergence between the output distributions of the teacher model and the student model; λ1 and λ2 are the weight coefficients, which adjust the balance between task learning and distillation learning.

[0045] Preferably, the automated deployment and collaborative inference are as follows:

[0046] Design a one-key automatic deployment tool to simplify the deployment process from the optimized model to the actual operating environment: by integrating multiple deployment environments such as cloud servers, edge devices, and local hardware, automatically identify the performance parameters of the computing power, memory, and network bandwidth of the target hardware, and generate the optimal deployment plan accordingly; specifically as follows: for tasks with high real-time requirements, that is, online customer support, preferentially select to deploy the lightweight model to the edge device while retaining the cloud support for complex tasks;

[0047] For embedded devices with limited storage, ensure the deployment adaptability through model compression and resource allocation.

[0048] Achieve efficient computing through dynamic load distribution and inference path optimization: in a multi-task scenario, dynamically adjust the inference strategy according to the priority, complexity, and real-time requirements of the tasks; specifically as follows:

[0049] For latency-sensitive real-time tasks, the module arranges the lightweight inference part on the edge device, and entrusts the high-complexity inference tasks (such as in-depth semantic analysis in multi-turn conversations) to cloud computing to ensure the optimal overall response time;

[0050] Support cloud-edge-device collaborative operation and improve task processing efficiency and system scalability through a distributed inference mechanism: During collaborative inference, the edge device first completes lightweight computing and returns preliminary results, and the cloud model then performs in-depth supplementation based on the edge results to achieve a balance between real-time response and high-precision output. Specifically as follows: In the legal document analysis scenario, the edge device can extract the core keywords in the user input and quickly return basic search results, while the cloud completes the analysis of the relevance of articles and complex logical reasoning, and finally synthesizes a complete answer.

[0051] More preferably, the monitoring and adaptive optimization are as follows:

[0052] Integrate real-time performance monitoring functions to perform multi-dimensional tracking of key indicators such as response time, memory occupancy, inference accuracy, and calculation latency for the running state of the model: These data will be dynamically collected during the inference process and the running state will be displayed in real time through a visual dashboard to help users grasp the current performance of the model. Specifically as follows:

[0053] For the model running on the edge device, when it is monitored that the memory usage is approaching the critical value, an alarm is issued in advance and the inference process is automatically adjusted to avoid resource overflow;

[0054] For complex models deployed in the cloud, track the load balancing state to ensure the rationality of multi-task allocation.

[0055] Achieve adaptive adjustment of running parameters by analyzing monitoring data: During the inference process, dynamically adjust the inference path and calculation scale of the model according to the current device load. Specifically as follows:

[0056] When the task complexity is lower than the set threshold, automatically switch to the simplified path mode and only call the lightweight model to complete the task;

[0057] When the task complexity is not lower than the set threshold or the accuracy requirement is higher than the set threshold, activate full-path inference and call the complete model for calculation;

[0058] During the monitoring and adaptive optimization process, it also supports automatic update and continuous optimization functions to ensure that the performance of the model remains optimal during long-term operation: When it is monitored that the inference accuracy of the model or the user satisfaction drops, automatically trigger the retraining or fine-tuning process to adapt to new task requirements or changes in the input distribution. Specifically as follows:

[0059] When new laws or case precedents are introduced in the legal consultation task, update the knowledge base in real time and fine-tune the model to ensure the timeliness and accuracy of the answer;

[0060] For the medical imaging scenario, when it is monitored that the diagnostic accuracy drops, call historical data for incremental update and dynamically supplement training data to optimize the model performance.

[0061] An Adaptive Optimization-based Domain-Specific Large Model Fine-Tuning and Deployment System, which includes:

[0062] A data processing and sample generation module, used to clean and structure specific domain data, and generate high-quality training samples through domain rules and feature extraction;

[0063] A few-shot fine-tuning and transfer module, used to achieve the efficient adaptation of the large model to the target domain through dynamic Prompt optimization, few-shot learning techniques, and cross-domain transfer;

[0064] A model compression and optimization module, used to reduce the model size and improve the running efficiency in resource-constrained environments by using task-aware pruning, multi-scale quantization, and knowledge distillation techniques;

[0065] An automated deployment and collaborative inference module, used to deploy the model to the cloud, edge, or local device through a one-key deployment tool, and perform task allocation and inference path optimization according to scenario requirements;

[0066] A monitoring and adaptive optimization module, used to monitor the system running status in real time, dynamically adjust the model parameters according to the feedback, and trigger the automatic update or re-fine-tuning process when the performance deteriorates to ensure long-term stable operation.

[0067] Preferably, the data processing and sample generation module performs feature extraction and logical extension on the domain data, extracts the core information in the text or image through named entity recognition (NER), TF-IDF, and domain rule parsing, and generates high-quality samples with diversity and logical consistency; and transforms the original data through data augmentation techniques, specifically: performing semantic replacement or sentence pattern reconstruction on the text, and injecting noise or adjusting the perspective on the image data, so as to generate training samples adapted to the domain task; through the guidance of specific domain rules, a small sample dataset is expanded in the case of scarce data, significantly improving the efficiency and effect of subsequent model fine-tuning;

[0068] The few-shot fine-tuning and transfer module achieves the efficient adaptation of the target domain through dynamic Prompt design and adaptive parameter optimization, and automatically generates dynamic Prompts according to the domain task, specifically: embedding relevant articles and case backgrounds in the legal domain, and adding patient report information in the medical domain to enhance semantic understanding ability; freezing the parameters irrelevant to the target domain in the pre-trained model, and only fine-tuning the highly relevant layers, thereby reducing the dependence on the labeled data in the target domain; at the same time, using cross-domain transfer techniques to extract the common features of the source domain and map them to the target domain, improving the generalization ability and task adaptation efficiency in the few-shot scenario;

[0069] The model compression and optimization module combines task-aware pruning, multi-scale quantization, and knowledge distillation techniques to optimize the model; by calculating the importance score of each parameter for the target task, it prunes parameters with low contribution while retaining the critical path to ensure model performance; the quantization technique dynamically adjusts the quantization bit width according to the importance of the model layer, specifically: using 8-bit representation for critical layers and 4-bit representation for secondary layers, thus achieving a balance between performance and compression ratio; at the same time, through knowledge distillation, it uses the fine-tuned large model to generate soft labels and transfers domain-specific knowledge to the lightweight student model to maintain high adaptability and efficient operation ability for domain tasks in resource-constrained environments;

[0070] The automated deployment and collaborative inference module provides a one-key deployment function by analyzing the target hardware environment and task requirements, and automatically adjusts the model's calculation path and task allocation according to the hardware performance. For example, it executes lightweight inference tasks on edge devices and delegates high-complexity calculations to the cloud; through the cloud-edge-end collaborative inference mechanism, the edge device quickly returns preliminary results, and the cloud performs in-depth supplementary inference and synthesizes the final output; it also supports the migration prediction function, generating an optimal deployment plan by analyzing the target deployment environment, significantly shortening the deployment time and improving the system operation efficiency;

[0071] The monitoring and adaptive optimization module performs multi-dimensional monitoring by collecting real-time model operation performance data (such as inference time, memory occupancy, and accuracy), and displays the performance status through a visual dashboard; at the same time, it dynamically adjusts the operation parameters according to the real-time monitoring data, such as switching the inference path or adjusting the context window size of the model, to adapt to the complexity of different tasks; when it monitors a decline in model performance or a change in the input distribution, it automatically triggers an update process to optimize the model through incremental training or re-fine-tuning, thus ensuring stability and reliability during long-term operation.

[0072] An electronic device, comprising: a memory and at least one processor;

[0073] Wherein, a computer program is stored on the memory;

[0074] The at least one processor executes the computer program stored in the memory, such that the at least one processor executes the method for fine-tuning and deploying a domain-specific large model based on adaptive optimization as described above.

[0075] A computer-readable storage medium, in which a computer program is stored, and the computer program can be executed by a processor to implement the method for fine-tuning and deploying a domain-specific large model based on adaptive optimization as described above.

[0076] The method and system for fine-tuning and deploying a domain-specific large model based on adaptive optimization of the present invention have the following advantages:

[0077] (1) The present invention solves the problems of domain adaptation of large models in small-sample scenarios and deployment in resource-constrained environments. Through innovative technologies such as domain feature-guided sample generation, dynamic Prompt optimization, cross-domain transfer learning, adaptive weight allocation, and knowledge dynamic fusion, it realizes efficient fine-tuning of small-scale domain data; and uses task-aware model pruning, multi-scale quantization, and knowledge distillation technologies to optimize the model scale and performance to adapt to the requirements of different computing environments; In addition, the present invention integrates a one-key cloud-edge-end collaborative deployment platform, which improves the deployment efficiency and performance of the model in multi-scenarios through automated migration prediction and resource optimization strategies, and is applicable to complex task scenarios with high requirements for domain knowledge such as legal consultation, medical diagnosis, and financial analysis, significantly improving the adaptation ability and operation efficiency of the model, and promoting the popularization and application of large model technology in domain-specific scenarios;

[0078] (2) The present invention integrates small-sample learning, model compression, knowledge distillation, and automated deployment optimization technologies, aiming to improve the adaptation ability of large models in small-sample data scenarios, and at the same time solve the problem of efficient operation of the model in resource-constrained environments. It is widely applicable to intelligent services and decision support in industries such as law, medicine, and finance, and has important technical value especially in scenarios with high requirements for domain specialization and model deployment efficiency;

[0079] (3) The present invention takes the efficient fine-tuning, optimization, and deployment of domain-specific large models as the core, and through modular design and key technology innovation, forms a complete solution from data preprocessing to model deployment. The system is based on five core modules: domain feature-guided sample generation, few-shot fine-tuning and transfer learning, model compression and optimization, one-key automated deployment, and real-time monitoring and dynamic adjustment. Combined with adaptive optimization strategies, it realizes efficient domain adaptation and resource optimization of large models under the conditions of scarce data and limited computing resources;

[0080] (4) The present invention improves the domain adaptation ability: through dynamic Prompt optimization, few-shot learning, and cross-domain transfer learning technologies, the system can achieve efficient domain adaptation of large models under small-scale data conditions, significantly improving the semantic understanding and task performance ability for specific domains (such as law, medicine, finance, etc.), and solving the problem of insufficient adaptability of large models in professional domains;

[0081] (5) The present invention significantly reduces resource consumption: using task-aware pruning, multi-scale quantization, and knowledge distillation technologies, it effectively reduces the computing resource requirements and storage volume of the model, enabling it to operate efficiently in resource-constrained edge devices and embedded environments; at the same time, this optimization significantly reduces hardware dependence and operating costs on the basis of ensuring model performance;

[0082] (6) The present invention accelerates the deployment efficiency and multi-scenario adaptation: The designed one-key automatic deployment and cloud-edge-end collaborative inference mechanism simplifies the deployment process of the model from optimization to online, and through dynamic load distribution and inference path optimization, it meets the real-time and reliability requirements in multi-scenarios, improving the adaptability and operation efficiency of the model on multiple devices and platforms;

[0083] (7) The present invention ensures the long-term stability of the system: Through real-time performance monitoring, adaptive parameter optimization, and automatic update mechanism, the system can dynamically adjust operation parameters and adapt to changes in task requirements, ensuring stable performance during long-term operation, while reducing the risk of model performance degradation caused by changes in input distribution, providing continuous reliable support for complex scenarios;

[0084] (8) The present invention has broad application value: It can be widely applied to fields such as legal regulations interpretation, medical image diagnosis, financial contract analysis, etc., and has significant advantages especially in complex tasks with high requirements for efficiency, accuracy, and intelligence. By significantly improving the efficiency of content generation and task processing, the present invention helps to promote the intelligent transformation of the industry, reduce labor costs, and improve overall productivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] The present invention will be further described below with reference to the accompanying drawings.

[0086] Appendix Figure 1 It is a schematic structural diagram of a domain-specific large model fine-tuning and deployment system based on adaptive optimization. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0087] The method and system for fine-tuning and deploying a domain-specific large model based on adaptive optimization of the present invention will be described in detail below with reference to the accompanying drawings of the specification and specific embodiments.

[0088] Embodiment 1

[0089] This embodiment provides a method for fine-tuning and deploying a domain-specific large model based on adaptive optimization, and the method is as follows:

[0090] S1. Data processing and sample generation: Clean, extract features, and perform small sample expansion on domain data, and generate high-quality training samples through a domain feature guidance mechanism;

[0091] S2. Few-shot fine-tuning and transfer: Achieve rapid domain adaptation of the large model through few-shot learning techniques and dynamic Prompt optimization, and combine cross-domain transfer learning to reduce the dependence on large-scale labeled data;

[0092] S3. Model compression and optimization: Optimize the large model using task-aware pruning, multi-scale quantization, and knowledge distillation techniques to reduce the size and computational resource requirements of the large model;

[0093] S4. Automated Deployment and Collaborative Inference: Support the one-click deployment of large models from optimization to cloud-edge-end environments, and optimize the inference path in combination with domain characteristics to meet real-time requirements;

[0094] S5. Monitoring and Adaptive Optimization: Monitor the performance of large models in real time, dynamically adjust operating parameters according to feedback, and ensure the stability and efficiency of large models.

[0095] The data processing and sample generation in step S1 of this embodiment are specifically as follows:

[0096] S101. Through data cleaning and standardization processing, ensure the quality and consistency of input data, specifically as follows:

[0097] For the legal field, clean the redundant punctuation and noisy information with chaotic formats in legal documents, and segment, number, and parse the terms of the text;

[0098] For the medical field, perform denoising and normalization processing on image data, extract key fields in diagnostic reports, and unify the storage format;

[0099] Further decompose the cleaned text and image data into structured information, that is, extract the applicable scope of legal provisions or the lesion areas of medical images, and ensure that the input data meets the training requirements of large models;

[0100] S102. Automatically expand the few-shot dataset by analyzing domain rules and features, specifically as follows:

[0101] For the legal field, generate case samples for different applicable scenarios through stripe correlation analysis or expand sample diversity by adjusting the time, region, and background conditions of the case;

[0102] For the medical field, generate more variant samples by combining image enhancement techniques such as rotation, mirroring, or noise injection, and enrich text samples by semantic transformation such as synonym replacement or word order adjustment of diagnostic reports; This mechanism ensures that even in a small-scale dataset, the system can generate samples with consistent logic and prominent domain characteristics, thereby improving the adaptation ability of the model in few-shot scenarios;

[0103] S103. Improve diversity and representativeness through dynamic recombination and feature screening, specifically as follows:

[0104] In text processing, extract key features through TF-IDF and named entity recognition (NER) to construct a high-weight sample set;

[0105] In image processing, a convolutional neural network is used to enhance the extraction of regional features, and key lesions or specific regions are focused on training; through this process, the generated sample data not only enriches the coverage of the training data, but also significantly improves the efficiency and accuracy of subsequent model fine-tuning, providing high-quality input support for domain-specific tasks.

[0106] The few-shot fine-tuning and transfer in step S2 of this embodiment are as follows:

[0107] S201. Adopt few-shot learning technology to enhance the large model's understanding ability of domain-specific tasks through dynamic Prompt optimization: Generate a dynamic Prompt template according to the domain task, specifically as follows:

[0108] For the legal field, the Prompt template embeds the context information of article numbers and case backgrounds, guiding the large model to generate answers that are more in line with legal logic;

[0109] For the medical field, the Prompt template adds auxiliary information such as patient examination reports and historical diseases to improve the large model's adaptability to diagnostic tasks;

[0110] At the same time, the Prompt template can dynamically adjust the content weights, specifically: in long text tasks, increase the weight of core keywords to ensure that the large model focuses on key details;

[0111] S202. Use the general features of the source domain to support the few-shot tasks of the target domain: Freeze the layers in the pre-trained large model that have a correlation with the target task lower than the set threshold, and only adjust the parameters for the highly relevant layers, thereby reducing the dependence on large-scale labeled data in the target domain; specifically: when migrating from the financial contract summary task to the legal contract analysis task, extract the general ability of text summarization and only optimize the legal clause parsing part; this mechanism significantly reduces the fine-tuning cost through the commonality analysis of the features of the source domain and the target domain, while improving the domain adaptation effect and generalization ability of the model; in addition, it also supports sharing training results among multiple related domains, such as migrating from image diagnosis to text report generation in the medical field, further expanding the application scope of the model;

[0112] S203. Combine the adaptive parameter adjustment mechanism to dynamically adjust the training strategy according to the task complexity: By analyzing the task length, keyword distribution, and semantic complexity features, calculate the parameter adjustment weight in real time; specifically as follows:

[0113] For complex tasks, the large model increases the learning rate step size, extends the context window, and enhances the ability to capture multi-dimensional information;

[0114] For simple tasks, compress the training step size, quickly complete the optimization, and avoid resource waste; through this mechanism, the system can achieve efficient domain fine-tuning in different scenarios and significantly improve the few-shot learning ability of the model, providing accurate support for domain-specific tasks.

[0115] Model compression and optimization in step S3 of this embodiment: Through task-aware pruning, multi-scale quantization, and knowledge distillation techniques, significantly reduce the model size and computational resource requirements while ensuring that the performance of the target task remains unchanged; specifically as follows:

[0116] S301. Adopt task-aware pruning technology. By dynamically evaluating the importance of parameters in the large model, prune the redundant parts and only retain the key parameters that have a greater impact on the task performance; among them, the importance scoring formula for pruning is as follows:

[0117]

[0118] Among them, I(w i ) represents the comprehensive importance score of the parameter; represents the loss function of the target task; Var(w i ) is the variance of the parameter distribution, reflecting the dynamic characteristics of the parameter in the model; CosSim(w i , w j ) is the cosine similarity between parameter w i and other parameter w j ; α and β are adjustable weights to balance the influence of gradient contribution, distribution dynamics, and redundant similarity; by calculating through comprehensive gradient, parameter distribution, and redundant information, the model can dynamically remove redundant parameters while maintaining the critical path for task performance;

[0119] S302. Use multi-scale quantization technology to optimize the weight representation. Adopt different quantization precisions for different parts of the model to compress the model to the greatest extent; among them, the quantization process is achieved by minimizing the total quantization error, and the optimization objective is:

[0120]

[0121] Among them, w i is the weight in the model; Q(w i , b) is the quantization function; b represents the quantization bit width (such as 4-bit or 8-bit); Equant is the quantization error function; is the regularization term, and γ is the weight coefficient, used to avoid performance degradation caused by over-compression of the model; by adjusting b and γ, dynamically allocate the quantization bit width according to task requirements to achieve a balance between high performance and high compression ratio;

[0122] S303. Use the fine-tuned large model as the teacher model through knowledge distillation technology, and generate soft labels to guide the small model to learn key features. Among them, the distillation process combines the task loss and the distillation loss, and the joint optimization objective formula is as follows:

[0123]

[0124] Among them, L task represents the loss of the target task; T represents the distillation temperature, which controls the smoothness of the output distribution of the teacher model; KL(p t , p s ) represents the Kullback-Leibler divergence between the output distributions of the teacher model and the student model; λ1 and λ2 are weight coefficients that adjust the balance between task learning and distillation learning.

[0125] The automated deployment and collaborative inference in step S4 of this embodiment aims to achieve the rapid deployment and efficient operation of the optimized large model in multiple scenarios, and at the same time meet different computing environments and task requirements through the cloud-edge-end collaboration mechanism. This module provides a complete solution from deployment to actual application, from automated deployment tools, one-click inference optimization to collaborative operation mechanism design, as follows:

[0126] S401. Design a one-click automatic deployment tool to simplify the deployment process from the optimized model to the actual operating environment: By integrating multiple deployment environments such as cloud servers, edge devices, and local hardware, automatically identify the performance parameters of the computing power, memory, and network bandwidth of the target hardware, and generate the optimal deployment plan accordingly, as follows:

[0127] For tasks with high real-time requirements, such as online customer support, preferentially deploy the lightweight model to the edge device while retaining cloud support for complex tasks;

[0128] For embedded devices with limited storage, ensure deployment adaptability through model compression and resource allocation.

[0129] S402. Achieve efficient computing through dynamic load distribution and inference path optimization: In a multi-task scenario, dynamically adjust the inference strategy according to the priority, complexity, and real-time requirements of the tasks, as follows:

[0130] For latency-sensitive real-time tasks, the module arranges the lightweight inference part on the edge device and entrusts high-complexity inference tasks (such as in-depth semantic analysis in multi-turn conversations) to cloud computing to ensure the optimal overall response time. In addition, dynamically adjust the inference load through the load balancing algorithm to avoid performance degradation caused by task congestion and improve the reliability of the overall system;

[0131] S403. Support cloud-edge-end collaborative operation and improve task processing efficiency and system scalability through a distributed inference mechanism: During collaborative inference, the edge device first completes lightweight calculations and returns preliminary results, and the cloud model then performs in-depth supplementation based on the edge results, thus achieving a balance between real-time response and high-precision output. Specifically as follows: In the legal document analysis scenario, the edge device can extract the core keywords in the user input, quickly return basic search results, while the cloud completes the analysis of article relevance and complex logical reasoning, and finally synthesizes a complete answer. This distributed collaboration significantly reduces the computational pressure on a single node and improves the overall response speed of the system.

[0132] Monitoring and adaptive optimization in step S5 of this embodiment: The monitoring and adaptive optimization module is the guarantee module for the operation of the entire system. It aims to ensure the efficient and stable operation of the model after deployment through real-time monitoring and dynamic adjustment mechanisms, and at the same time achieve adaptive optimization according to task requirements and usage scenarios to improve the long-life cycle performance of the model. Specifically as follows:

[0133] S501. Integrate real-time performance monitoring functions to perform multi-dimensional tracking of key indicators such as response time, memory occupancy, inference accuracy, and calculation latency on the running state of the model: These data will be dynamically collected during the inference process and the running state will be displayed in real time through a visual dashboard to help users master the current performance of the model. Specifically as follows:

[0134] For the model running on the edge device, when it is monitored that the memory usage is close to the critical value, an alarm is issued in advance and the inference process is automatically adjusted to avoid resource overflow.

[0135] For complex models deployed in the cloud, track the load balancing state to ensure the rationality of multi-task allocation.

[0136] S502. Achieve adaptive adjustment of running parameters by analyzing monitoring data: During the inference process, dynamically adjust the inference path and calculation scale of the model according to the current device load. Specifically as follows:

[0137] When the task complexity is lower than the set threshold, automatically switch to the simplified path mode and only call the lightweight model to complete the task.

[0138] When the task complexity is not lower than the set threshold or the accuracy requirement is higher than the set threshold, activate full-path inference and call the complete model for calculation, which not only improves the running efficiency but also reduces unnecessary resource consumption, adapting to the real-time requirements of different tasks.

[0139] During the S503 monitoring and adaptive optimization process, the automatic update and continuous optimization functions are also supported to ensure that the model maintains optimal performance during long-term operation: when it is monitored that the inference accuracy or user satisfaction of the model decreases, the retraining or fine-tuning process is automatically triggered to adapt to new task requirements or changes in the input distribution; specifically as follows:

[0140] When new regulations or case laws are introduced in legal consultation tasks, the knowledge base is updated in real time and the model is fine-tuned to ensure the timeliness and accuracy of the answers;

[0141] For the medical imaging scenario, when it is monitored that the diagnostic accuracy decreases, historical data is called for incremental update, and training data is dynamically supplemented to optimize the model performance.

[0142] Example 2:

[0143] As shown in the attached Figure 1 figure, this embodiment provides a domain-specific large model fine-tuning and deployment system based on adaptive optimization, and the system includes:

[0144] A data processing and sample generation module, which is used to clean and structurally process specific domain data, and generate high-quality training samples through domain rules and feature extraction;

[0145] A few-shot fine-tuning and transfer module, which is used to achieve the efficient adaptation of the large model to the target domain through dynamic Prompt optimization, few-shot learning technology and cross-domain transfer;

[0146] A model compression and optimization module, which is used to reduce the model volume and improve the operation efficiency in resource-constrained environments by using task-aware pruning, multi-scale quantization and knowledge distillation technologies;

[0147] An automated deployment and collaborative inference module, which is used to deploy the model to the cloud, edge or local device through a one-key deployment tool, and perform task allocation and inference path optimization according to the scenario requirements;

[0148] A monitoring and adaptive optimization module, which is used to monitor the system operation status in real time, dynamically adjust the model parameters according to the feedback, and trigger the automatic update or re-fine-tuning process when the performance decreases to ensure long-term stable operation.

[0149] In this embodiment, the data processing and sample generation module extracts features and performs logical expansion on domain data. It extracts the core information in text or images through named entity recognition (NER), TF-IDF, and domain rule parsing, and generates high-quality samples with diversity and logical consistency. It also transforms the original data through data augmentation techniques, specifically: performing semantic substitution or sentence restructuring on text, and injecting noise or adjusting the perspective on image data, so as to generate training samples suitable for domain tasks. Through the guidance of domain-specific rules, a small sample dataset is expanded in the case of scarce data, significantly improving the efficiency and effect of subsequent model fine-tuning.

[0150] In this embodiment, the few-shot fine-tuning and transfer module achieves efficient adaptation to the target domain through dynamic Prompt design and adaptive parameter optimization, and automatically generates dynamic Prompts according to domain tasks, specifically: embedding relevant articles and case backgrounds in the legal domain, and adding patient report information in the medical domain to enhance semantic understanding ability; freezing the parameters irrelevant to the target domain in the pre-trained model, and only fine-tuning the highly relevant layers, thus reducing the dependence on labeled data in the target domain; at the same time, using cross-domain transfer technology to extract the common features of the source domain and map them to the target domain, improving the generalization ability and task adaptation efficiency in the few-shot scenario.

[0151] In this embodiment, the model compression and optimization module combines task-aware pruning, multi-scale quantization, and knowledge distillation techniques to optimize the model; and calculates the importance score of each parameter for the target task, pruning the parameters with low contribution while retaining the critical path to ensure model performance; the quantization technique dynamically adjusts the quantization bit width according to the importance of the model layer, specifically: using 8-bit representation for the critical layer and 4-bit representation for the secondary layer, thus achieving a balance between performance and compression ratio; at the same time, through knowledge distillation, using the fine-tuned large model to generate soft labels and transferring domain-specific knowledge to the lightweight student model to maintain high adaptability and efficient operation ability for domain tasks in resource-constrained environments.

[0152] In this embodiment, the automated deployment and collaborative inference module provides a one-click deployment function by analyzing the target hardware environment and task requirements, and automatically adjusts the model's calculation path and task allocation according to the hardware performance. For example, it executes lightweight inference tasks on edge devices and delegates high-complexity calculations to the cloud; through the cloud-edge-end collaborative inference mechanism, the edge device quickly returns preliminary results, and the cloud performs in-depth supplementary inference and synthesizes the final output; it also supports the migration prediction function, generating the optimal deployment plan by analyzing the target deployment environment, significantly shortening the deployment time and improving the system operation efficiency.

[0153] In this embodiment, the monitoring and adaptive optimization module performs multi-dimensional monitoring by collecting model running performance data in real time (such as inference time, memory occupancy, and accuracy), and displays the performance status through a visual dashboard; at the same time, it dynamically adjusts the running parameters according to the real-time monitoring data, such as switching the inference path or adjusting the context window size of the model, to adapt to the complexity of different tasks; when it monitors a decrease in model performance or a change in the input distribution, it automatically triggers an update process to optimize the model through incremental training or re-fine-tuning, so as to ensure stability and reliability during long-term operation.

[0154] Embodiment 3:

[0155] This embodiment also provides an electronic device, including: a memory and a processor;

[0156] Wherein, the memory stores computer execution instructions;

[0157] The processor executes the computer execution instructions stored in the memory, so that the processor executes the method for fine-tuning and deploying a domain-specific large model based on adaptive optimization in any embodiment of the present invention.

[0158] The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0159] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory may also include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, at least one magnetic disk storage period, a flash memory device, or other volatile solid-state storage devices.

[0160] Embodiment 4:

[0161] This embodiment also provides a computer-readable storage medium storing multiple instructions that are loaded by a processor to cause the processor to execute the method for fine-tuning and deploying a domain-specific large model based on adaptive optimization according to any embodiment of the present invention. Specifically, a system or device equipped with a storage medium can be provided, on which software program code for implementing the functions of any of the above embodiments is stored, and the computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage medium.

[0162] In this case, the program code read from the storage medium itself can implement the functions of any of the above embodiments, so the program code and the storage medium storing the program code constitute a part of the present invention.

[0163] Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RYM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Optionally, the program code can be downloaded from a server computer via a communication network.

[0164] Furthermore, it should be clear that not only can the functions of any of the above embodiments be implemented by executing the program code read by the computer, but also by the operating system or the like operating on the computer based on the instructions of the program code to complete part or all of the actual operations.

[0165] In addition, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or the memory provided in the expansion unit connected to the computer, and then based on the instructions of the program code, the CPU or the like installed on the expansion board or the expansion unit executes part and all of the actual operations, thereby implementing the functions of any of the above embodiments.

[0166] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features. However, such modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A domain-specific large model fine-tuning and deployment method based on adaptive optimization, characterized in that: The method is as follows: Data processing and sample generation: clean domain data, extract features, and expand small samples, and generate high-quality training samples through domain feature guidance mechanisms; Few-sample fine-tuning and transfer: The fast domain adaptation of large models is achieved through few-sample learning technology and dynamic prompt optimization, and cross-domain transfer learning is combined to reduce the dependence on large-scale labeled data; Model compression and optimization: Use task-aware pruning, multi-scale quantization, and knowledge distillation techniques to optimize large models, reducing their size and computing resource requirements; Automated deployment and collaborative reasoning: Supports one-click deployment of large models from optimization to cloud-edge-end environments, and optimizes reasoning paths based on domain characteristics; Monitoring and adaptive optimization: Monitor large model performance in real time and dynamically adjust operating parameters based on feedback.

2. The method for fine-tuning and deploying a domain-specific large model based on adaptive optimization according to claim 1, characterized in that: The data processing and sample generation are as follows: Ensure the quality and consistency of input data through data cleaning and standardization, as follows: In the legal field, we clean up unnecessary punctuation and chaotically formatted noise information in legal documents, and segment, number and parse the text into clauses; In the medical field, image data is denoised and normalized, and key fields in the diagnosis report are extracted and stored in a unified format; The cleaned text and image data are further decomposed into structured information, that is, the scope of application of legal provisions or the lesion area of ​​medical images are extracted to ensure that the input data meets the training requirements of the large model; By analyzing domain rules and features, the few-sample dataset is automatically expanded as follows: In the legal field, case samples of different applicable scenarios are generated through stripe correlation analysis or the time, region and background conditions of the case are adjusted to expand sample diversity; In the medical field, image enhancement techniques such as rotation, mirroring or noise injection are combined to generate variant samples, while semantic transformations such as synonym replacement or word order adjustment of diagnostic reports are used to enrich text samples. Improve diversity and representation through dynamic recombination and feature selection as follows: In text processing, key features are extracted through TF-IDF and named entity recognition to build a high-weight sample set; In image processing, convolutional neural networks are used to enhance the extraction of regional features, with a focus on training key lesions or specific areas.

3. The method for fine-tuning and deploying a domain-specific large model based on adaptive optimization according to claim 1, characterized in that: The details of few-sample fine-tuning and migration are as follows: Using few-sample learning technology, dynamic prompt optimization is used to enhance the large model's ability to understand domain-specific tasks: dynamic prompt templates are generated according to domain tasks, as follows: In the legal field, the prompt template embeds contextual information of the article number and case background, guiding the big model to generate answers that are more in line with legal logic; In the medical field, the prompt template adds auxiliary information such as patient examination reports and historical symptoms to improve the adaptability of the large model to diagnostic tasks; At the same time, the Prompt template can dynamically adjust the content weight, specifically: increase the weight of core keywords in long text tasks to ensure that the large model focuses on key details; Use the common features of the source domain to support the few-sample tasks in the target domain: freeze the layers in the pre-trained large model whose relevance to the target task is lower than the set threshold, and only adjust the parameters of the highly relevant layers, thereby reducing the reliance on large-scale annotated data in the target domain; specifically: when migrating from the financial contract summary task to the legal contract analysis task, extract the common capabilities of the text summary and only optimize the legal clause parsing part; Combined with the adaptive parameter adjustment mechanism, the training strategy is dynamically adjusted according to the task complexity: by analyzing the task length, keyword distribution and semantic complexity characteristics, the parameter adjustment weight is calculated in real time; the details are as follows: For complex tasks, large models increase the learning rate step size, extend the context window, and enhance the ability to capture multi-dimensional information; For simple tasks, the training step size is compressed to quickly complete the optimization.

4. The method for fine-tuning and deploying a domain-specific large model based on adaptive optimization according to claim 1, characterized in that: Model compression and optimization are as follows: The task-aware pruning technology is used to dynamically evaluate the importance of parameters in the large model, prune the redundant parts, and only retain the key parameters that have a greater impact on task performance; the importance scoring formula for pruning is as follows: Among them, I(w i ) represents the comprehensive importance score of the parameter; represents the loss function of the target task; Var(w i ) is the variance of parameter distribution, reflecting the dynamic characteristics of parameters in the model; CosSim(w i ,w j ) is the parameter w i and other parameters w j cosine similarity; α and β are adjustable weights that balance the impact of gradient contribution, distribution dynamics, and redundant similarity; by combining gradient, parameter distribution, and redundant information for calculation, the model can dynamically remove redundant parameters while maintaining the critical path to task performance; The multi-scale quantization technology is used to optimize the weight representation, and different quantization precisions are used for different parts of the model to maximize the compression of the model; the quantization process is achieved by minimizing the total quantization error, and the optimization goal is: Among them, w i is the weight in the model; Q(w i ,b) is the quantization function; b represents the quantization bit width; Equant is the quantization error function; is a regularization term, and γ is a weight coefficient, which is used to avoid performance degradation caused by excessive compression of the model. By adjusting b and γ, the quantization bit width is dynamically allocated according to task requirements to achieve a balance between high performance and high compression rate. The fine-tuned large model is used as the teacher model through knowledge distillation technology, and the small model is guided to learn key features through soft label generation. The distillation process combines the task loss and the distillation loss, and the joint optimization objective formula is as follows: Among them, L task represents the loss of the target task; T represents the distillation temperature, which controls the smoothness of the output distribution of the teacher model; KL(p t ,p s ) represents the Kullback-Leibler divergence between the output distributions of the teacher model and the student model; λ1 and λ2 are weight coefficients that adjust the balance between task learning and distillation learning.

5. The method for fine-tuning and deploying a domain-specific large model based on adaptive optimization according to claim 1, characterized in that: The details of automated deployment and collaborative reasoning are as follows: Design a one-click automatic deployment tool to simplify the deployment process from optimizing models to actual operating environments: By integrating multiple deployment environments such as cloud servers, edge devices, and local hardware, automatically identify the performance parameters of the target hardware's computing power, memory, and network bandwidth, and generate the optimal deployment plan accordingly; specifically: For tasks with high real-time requirements, such as online customer support, prioritize deploying lightweight models to edge devices while retaining cloud support for complex tasks; For embedded devices with limited storage, ensure deployment adaptability through model compression and resource allocation. Efficient computing through dynamic load distribution and inference path optimization: In multi-task scenarios, the inference strategy is dynamically adjusted according to the priority, complexity and real-time requirements of the task; the details are as follows: For latency-sensitive real-time tasks, the module schedules lightweight reasoning on edge devices and delegates high-complexity reasoning tasks to cloud computing to ensure optimal overall response time; It supports cloud-edge-end collaborative operation and improves task processing efficiency and system scalability through distributed reasoning mechanism: in the collaborative reasoning process, the edge device first completes lightweight calculations and returns preliminary results, and the cloud model then deeply supplements the edge results, thereby achieving a balance between real-time response and high-precision output; specifically: in the legal document analysis scenario, the edge device can extract core keywords in the user input and quickly return basic search results. At the same time, the cloud completes the clause relevance analysis and complex logical reasoning, and finally synthesizes a complete answer.

6. The method for fine-tuning and deploying a domain-specific large model based on adaptive optimization according to any one of claims 1 to 5, characterized in that: Monitoring and adaptive optimization are as follows: The integrated real-time performance monitoring function tracks the model's operating status in multiple dimensions, including key indicators such as response time, memory usage, inference accuracy, and computational latency. This data is dynamically collected during the inference process, and the operating status is displayed in real time through a visual dashboard to help users understand the current performance of the model. The details are as follows: For models running on edge devices, when monitoring memory usage close to a critical value, an alert is issued in advance and the inference process is automatically adjusted to avoid resource overflow; For complex models deployed in the cloud, track the load balancing status to ensure the rationality of multi-task allocation. Adaptive adjustment of operating parameters is achieved by analyzing monitoring data: During the inference process, the inference path and calculation scale of the model are dynamically adjusted according to the current equipment load, as follows: When the task complexity is lower than the set threshold, it automatically switches to the simplified path mode and only calls the lightweight model to complete the task; When the task complexity is not less than the set threshold or the accuracy requirement is higher than the set threshold, the full path reasoning is activated and the complete model is called for calculation; During the monitoring and adaptive optimization process, automatic updates and continuous optimization functions are also supported to ensure that the performance of the model remains optimal in the long run: when the reasoning accuracy of the model or the user satisfaction is monitored to decrease, the retraining or fine-tuning process is automatically triggered to adapt to new task requirements or changes in input distribution; the details are as follows: When new regulations or precedents are introduced in legal consultation tasks, the knowledge base is updated in real time and the model is fine-tuned to ensure the timeliness and accuracy of the answers; For medical imaging scenarios, when the diagnostic accuracy is monitored to decrease, historical data is called for incremental updates, and training data is dynamically supplemented to optimize model performance.

7. A domain-specific large model fine-tuning and deployment system based on adaptive optimization, characterized in that: The system includes: The data processing and sample generation module is used to clean and structure data in a specific field and generate high-quality training samples through field rules and feature extraction; The few-shot fine-tuning and migration module is used to achieve efficient adaptation of large models to target domains through dynamic prompt optimization, few-shot learning technology, and cross-domain migration; Model compression and optimization module, which uses task-aware pruning, multi-scale quantization, and knowledge distillation techniques to reduce model size and improve operating efficiency in resource-constrained environments; The automated deployment and collaborative reasoning module is used to deploy models to the cloud, edge, or local devices through a one-click deployment tool, and to perform task allocation and reasoning path optimization based on scenario requirements; The monitoring and adaptive optimization module is used to monitor the system operation status in real time, dynamically adjust the model parameters according to the feedback, and trigger the automatic update or re-fine-tuning process when the performance degrades to ensure long-term stable operation.

8. The domain-specific large model fine-tuning and deployment system based on adaptive optimization according to claim 7 is characterized in that: The data processing and sample generation module performs feature extraction and logical expansion on the domain data, extracts the core information in the text or image through named entity recognition, TF-IDF and domain rule parsing, and generates high-quality samples with diversity and logical consistency; and transforms the original data through data enhancement technology, specifically: semantic replacement or sentence reconstruction of the text, noise injection or perspective adjustment of the image data, so as to generate training samples adapted to the domain task; The few-sample fine-tuning and migration module achieves efficient adaptation to the target domain through dynamic prompt design and adaptive parameter optimization, and automatically generates dynamic prompts according to domain tasks, specifically: embedding relevant provisions and case background in the legal field, and adding patient report information in the medical field to enhance semantic understanding capabilities; The model compression and optimization module combines task-aware pruning, multi-scale quantization, and knowledge distillation techniques to optimize the model. It also calculates the importance score of each parameter to the target task, prunes parameters with low contribution, and retains the key path to ensure model performance. The quantization technology dynamically adjusts the quantization bit width according to the importance of the model layer, specifically: 8-bit representation for the key layer and 4-bit representation for the secondary layer, so as to achieve a balance between performance and compression rate. At the same time, through knowledge distillation, soft labels are generated using the fine-tuned large model to migrate domain-specific knowledge to the lightweight student model to maintain high adaptability and efficient operation capabilities of domain tasks in resource-constrained environments. The automated deployment and collaborative reasoning module provides one-click deployment by analyzing the target hardware environment and task requirements, and automatically adjusts the model's calculation path and task allocation according to hardware performance. Through the cloud-edge-end collaborative reasoning mechanism, the edge device quickly returns preliminary results, and the cloud performs deep supplementary reasoning and synthesizes the final output. It also supports migration prediction function, which generates the optimal deployment plan by analyzing the target deployment environment, significantly shortening the deployment time and improving system operation efficiency. The monitoring and adaptive optimization module collects model operation performance data in real time for multi-dimensional monitoring and displays the performance status through a visual dashboard. It also dynamically adjusts operation parameters based on real-time monitoring data, such as switching inference paths or adjusting the model's context window size to adapt to the complexity of different tasks. When it is monitored that model performance has degraded or input distribution has changed, the update process is automatically triggered to optimize the model through incremental training or re-fine-tuning, thereby ensuring stability and reliability in long-term operation.

9. An electronic device, characterized in that: include: memory and at least one processor; Wherein, the memory stores a computer program; The at least one processor executes the computer program stored in the memory, so that the at least one processor performs the domain-specific large model fine-tuning and deployment method based on adaptive optimization as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which can be executed by a processor to implement the domain-specific large model fine-tuning and deployment method based on adaptive optimization as described in any one of claims 1 to 6.

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