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Sophon LLMOps

Transwarp Sophon LLMOps

Corpus Management
Guided LLM Fine-tuning
Zero-code Application Development
Product Introduction
Sophon LLMOps is an enterprise-level LLM full life cycle operation and management platform launched by Transwarp, which aims to enable enterprise users to implementLLMs in production and business in an agile, efficient and closed-loop manner. Sophon LLMOps opens up and optimizes the full-link process of corpus access and development, prompt engineering, LLM fine-tuning, knowledge extraction and fusion, model management, application and intelligent agent construction, application deployment, operation and maintenance and monitoring, as well as alignment and improvement of business effects.
What does Sophon LLMOps offer?
Corpus processing and knowledge vectorization storage
Sophon LLMOps provides corpus full life cycle management capabilities such as large-scale corpus access, guided corpus cleaning, multi-team collaborative corpus annotation, corpus evaluation and operation; in the knowledge storage stage, the platform supports multiple file format parsing and vectorization, and supports professional parsing, sharding and retrieval output strategy configuration: supports users to publish the parsing process arranged by the application development module into a parsing service, and realizes more flexible document content extraction. In addition to self-built knowledge bases, the platform can also support docking with existing knowledge bases within the enterprise.
Model training and management
Sophon LLMOps supports unified management of multimodal (LLMs) models and third-party model services, unified interface specifications, and refined version management of different modal models. In terms of model training, the platform provides two LLM fine-tuning training methods: interface guidance and code programming. Based on the platform's model evaluation function, you can choose preset general evaluation rules or custom evaluation templates to evaluate indicators in various dimensions for the models that have been put on the shelves. In particular, Sophon LLMOps is still compatible with the full life cycle capabilities of training and management of traditional machine learning and deep learning models.
Application development and management
Sophon LLMOps provides four application building methods for different user types and usage scenarios: zero-code building similar to GPTs, low-code operator orchestration, and building LLM applications based on online programming and custom containers. For example, for operators, the platform supports users to quickly build RAG and Agent applications in a zero-code way. Users can publish the built application with one click for quick experience, share it with one click, and provide API calls to the outside world.
Service deployment and operation monitoring
Sophon LLMOps supports configuring elastic scaling strategies and dynamically scaling capacity to achieve load balancing. It also supports multi-version traffic distribution strategies and has grayscale release and A/B testing capabilities. During the monitoring and deployment phase, the platform supports real-time monitoring of the operating status of model services and controls model production environment risks, such as cluster resource utilization, throughput, response time, and access records.
Enterprise-level management capabilities
For enterprise organizations, Sophon LLMOps provides a series of enterprise-level capabilities including member permission control, resource monitoring, billing management, approval and auditing, security control, etc. Member management and permission control within the space ensure data isolation and data security between users, support users to effectively audit and approve the core processes of the platform such as model deployment, application startup and shutdown, and task operation, and enhance the traceability of operations and the security of the system.
Why choose Sophon LLMOps?
One-stop AI development
Sophon LLMOps not only provides tool capabilities for large language model and application development, but is also compatible with ML/DL model development and implementation. At the same time, it can flexibly embed "small model" capabilities in the application development process of LLMs, that is, a single platform can support the development of full AI scenario applications.
Rich application development methods
Sophon LLMOps provides four application development methods for different user types and usage scenarios: For business users, it provides a GPTs-like method that can be created by simply configuring the knowledge base and prompt words. For users with certain development capabilities, it provides a drag-and-drop method to orchestrate custom application chains and an online programming method to build applications. Finally, it also supports users to specify mirrors to pull up services and provides a unified access interface to the outside world.
Support the construction of high-quality knowledge base
Sophon LLMOps not only pre-sets standard corpus parsing strategies, but also allows users to build more flexible and sophisticated corpus parsing strategies through customized application chains and publish them as processing services for the complex and diverse unstructured data types in actual implementation scenarios.
Flexible and open platform use
At the model level, Sophon LLMOps supports one-click access to standard open source (LLMs) models, manages third-party model services, and performs traffic control; supports third-party knowledge bases to be connected to the platform in the form of standard interfaces; and provides a unified docking API and service monitoring management module for models, applications, and knowledge bases managed by the platform.
Comprehensive security protection
Sophon LLMOps builds a unified security center to provide all-round security protection for user input and model/application output. For example, it can judge user output through prompt word injection and sensitive word filtering to avoid inducing the model to output illegal subject content. This can effectively protect user privacy and improve the fairness and security of model/application output.
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