Hui Zheng Large Model

What is the Hui Zheng large model?

The Hui Zheng large model is an industry-oriented large model platform developed by Zhongke Huilian for use by government agencies.

It emphasizes the enhancement of government data, integration of business systems, use of domestic innovative technologies, and private deployment.

Product positioning

Special abilities

• It is compatible with domestic software and hardware, as well as with the environment for independent innovation in technology.
• It provides integrated capabilities for training and inference.
• Leverage government-owned data for credible enhancement.
• It supports private deployment and scenario customization.
• Connects the knowledge base, file repository, and statistical database.
• Combine standard Q&A with open-ended generation.
• Provides process guidance for government services.
• It covers office, service, public, regulatory, and governance scenarios.

Smart government assistance tool

The government affairs assistant provides staff with Q&A answers, tips, procedural guidance, and operational support.

• Inquire about policies, regulations, and internal procedures.
• Provide job-related task suggestions based on the position.
• The next steps in the process are determined based on the questions posed.
• Summarize the key points and differences across multiple documents.
• Generate a draft of official documents or reports.
• Connect to business systems within the scope of permission.

The assistant’s outputs should be based on valid policies and internal guidelines; they cannot replace staff members in making administrative decisions.

Multi-source government knowledge retrieval

The platform enables intelligent retrieval of various types of data, including knowledge bases, file repositories, and statistical databases.

1. Organize the data catalog, responsible departments, and update frequency.
2. Remove duplicate, expired, and conflicting content.
3. Establish labels for policy levels, validity periods, and applicable regions.
4. Configure access permissions at the user, department, and task levels.
5. Create indexes for documents, Q&A, and structured data.
6. Test the retrieval and recall performance using real business problems.
7. Have the response show the basis, version, and effective date.
8. Establish an operational loop for unknown issues and incorrect responses.

Smart government services consultation

Government consultations are developed by combining standard answer templates with open-ended questions, to serve both the public and staff members.

In cases related to individual treatment, penalties, permits, or legal rights, the official decision issued as a result of the formal process shall prevail.

Intelligent content creation

Content creation allows for the generation of draft governmental texts based on theme, style, and purpose.

• Outline of the official document and draft of the materials.
• Policy interpretation and draft of common questions.
• Government news and new media content.
• Meeting minutes and work summaries.
• Notifications, reminders, and service scripts.
• Multi-channel content rewriting and summarization.

Before publication, it must undergo reviews regarding facts, policies, confidentiality, wording, and approval processes.

Intelligent statistical decision-making

The platform can extract data as required for decision-making, generate analysis results, and produce recommendations.

• Aggregate statistical indicators and historical changes.
• Identify anomalies, hotspots, and associated factors.
• Create charts, summaries, and thematic reports.
• It assists in proposing recommendations for governance and service optimization.
• Analyze hotline calls, public opinion, and business data.

Model recommendations should not replace the confirmation of statistical criteria, expert assessments, and statutory decision-making processes.

Use cases

Domestic innovation in information technology and modeling capabilities

The official website lists domestic innovation in IT solutions, integrated training and deployment capabilities, and the compression of models with billions of parameters as the product’s advantages.

The manufacturer claims that compression preserves over 90% of the original model’s performance while enabling a 10-fold acceleration in inference speed.

These are public technical specifications; the purchaser should still conduct independent testing on the target hardware and actual data.

Private deployment

Private deployment is suitable for organizations that have high requirements regarding data isolation, network boundaries, and autonomous operation and maintenance.

Guide to Deploying the Hui Zheng Large Model

1. Identify business objectives, target customers, and responsible departments.
2. Identify the initial set of use cases that are high in value and have manageable risks.
3. Audit data, systems, computing power, and security conditions.
4. Establish rules for knowledge governance and access control.
5. Design models, search mechanisms, workflows, and system integration solutions.
6. Use masked samples for validation and small-scale pilot tests.
7. Complete tests for performance, security, accuracy, and availability.
8. Train business staff and establish a manual backup process.
9. Go live in accordance with the acceptance criteria and conduct continuous monitoring.
10. Regularly update knowledge, models, and risk strategies.

Knowledge governance

The reliability of government-related large models depends to a large extent on whether the knowledge used is authoritative, effective, and traceable.

• Each policy knowledge tag indicates the issuing authority and the level of validity.
• Record activation, deactivation, and substitution relationships.
• Items with the same name are distinguished by region and department.
• Important answers should use only verified knowledge.
• After the policy is updated, an review of the indexes and answers is carried out.
• Save records of editing, approval, and publication.

Safety and compliance

• Control access rights based on the classification and grading of data.
• Sensitive information is masked during data entry and in the logging process.
• High-risk operations require manual confirmation.
• Record the retrieval criteria, model version, and operation logs.
• Restrict the model from connecting to unauthorized external services.
• Conduct prompt injection, privilege escalation, and data leakage tests.
• Establish procedures for handling incorrect answers and security incidents.

Accuracy evaluation

Price and procurement methods

The information below was verified on August 31, 2026; the official website does not disclose the fixed prices of the packages.

The final price, scope of delivery, taxes and fees, as well as service commitments, shall be governed by the official quotation and contract.

Checklist before procurement

• Can the models and key components be integrated into existing domestic innovation environments?
• Who provides the computing power, and how is peak concurrency determined?
• Are the data leaving the specified network and region?
• Which features are part of the standard product, and which are custom?
• Who is responsible for knowledge updating and manual review?
• To what extent are the interfaces, secondary developments, and source code provided?
• How to handle upgrades, failures, vulnerabilities, and service disruptions?
• How to make corrections and settle the account when the acceptance criteria are not met?

Suggested acceptance criteria

• Use a real business question bank to evaluate accuracy.
• Test standard Q&A and open-ended generation separately.
• Verify scenarios with no answers, conflicts, and expired policies.
• Test concurrency, latency, fault recovery, and resource usage.
• Isolate verification departments, positions, and individual permissions.
• Check the logging, auditing, backup, and alerting capabilities.
• End-to-end business testing was carried out on the integrated interface.
• Confirm the delivery of training, documentation, and operations services.

API and open-source status

Usage restrictions

• The effectiveness of the project depends on the quality of government data and its ongoing maintenance.
• The generated content may contain omissions, misunderstandings, or outdated information.
• Statistical suggestions cannot replace formal data verification.
• In high-risk scenarios such as automatic approval, it is necessary to define clear boundaries for human intervention.
• Vendor specifications need to be independently verified in a real-world environment.
• Deployment, interface, and operation costs should be included in the overall budget.

Frequently Asked Questions

Is the Hui Zheng large model suitable for individual users?

It is not suitable; it is primarily aimed at government agencies, focusing on project development, system integration, and private deployment.

What are the core functions of the Hui Zheng large model?

These include government affairs assistants, multi-source knowledge retrieval, intelligent consulting, content creation, and statistical decision-making.

Does the Hui Zheng large model support private deployment?

Yes, the official website explicitly lists private deployment and full customization based on specific scenarios as part of the product’s capabilities.

Does the Hui Zheng large model support domestic innovative technologies?

Support is available; the official website states that the platform can function in domestic environments, but the specific list of compatible components must be determined as part of the project.

How much does the Hui Zheng large model cost?

The official website does not list any fixed packages; the costs are determined based on deployment, computing power, data management, integration, and the services provided.

Does the HuiZheng large model provide public APIs?

No public API pricing or development documentation has been found yet; the scope of the project’s interfaces needs to be confirmed with the vendor.

Is the Hui Zheng large model open source?

No, at present no official open-source code for a complete platform has been found; moreover, private deployment does not equate to being open source.