Question

Is there anyone who can deploy a large model of AI writing? What are the requirements for deploying large AI models?

Answer 1

Deploying AI writing large models usually requires considering several key factors, including hardware resources, model selection, deployment tools, and environment configuration. Here are some basic steps and recommendations:

1. Hardware resources: According to , using a consumer-grade graphics card such as NVIDIA 4090 with 24G video memory can run large models like 13B parameter level. The size of the video memory directly affects the size of the model that can be run.

2. Model selection: It is crucial to choose a suitable AI writing large model. According to , the recommended models include the Qianwen series, glm series, Baichuan series, and llama series. In particular, the Qwen and Qwen1.5 models of the Qianwen series are recommended for their agent capabilities and adaptability.

3. Software and library versions: If you choose the Qwen1.5 model, you need to pay attention to the version requirements of the transformers library mentioned in , that is, it needs to be greater than 4.37.0.

4. Simplified deployment tools: For situations where video memory is tight or you want to simplify the deployment process, you can use Ollama, which allows large models to be deployed using memory, and provides one-click deployment and installation for macOS and Windows, which is suitable for beginners.

5. Memory and video memory requirements: According to, running a 7B large model requires at least 8GB of memory, while a 33B large model requires 32GB of video memory.

6. Professional writing model: If you need more professional writing assistance, you can consider using the Weaver model, which is designed for creative writing and provides models of different sizes to suit different needs.

7. Platforms and tools: Using Baidu's BML platform, you can easily use the Wenxin large model, which includes NLP, CV, and cross-modal large models, supporting multiple language understanding and literary creation tasks.

8. Open source models and community support: Consider using open source models and community-supported resources such as MiniCPM-V and OmniLMM, as well as the large model deployment tutorial provided by WEBAI.

9. Private deployment: If private deployment is required, you can refer to the tutorial provided by Baidu Developer Center, which provides a guide to deploy efficient private large models with one click.

10. Mixed text and image creation: For application scenarios that require mixed text and image creation, you can consider using the Pu Yu Lingbi model launched by Shanghai AI Laboratory.

During the deployment process, you also need to consider the fine-tuning and personalization of the model, as well as how to integrate the model into specific applications. Be sure to comply with relevant laws, regulations and copyright policies to ensure the legal and compliant use of AI-created content.

Answer 2

The deployment requirements of large AI models cover multiple aspects, including hardware, software, data, security and compliance, and network. The following is a brief summary:

1. Hardware requirements

- Computing resources: High-performance GPU or TPU hardware support is required to meet the computing power requirements of model training and reasoning, and ensure that the computing power can cope with the complexity of the model and the scale of the data.

- Storage resources: Large models usually require a large amount of data storage, and high-speed, large-capacity storage devices are required to ensure efficient reading and writing of data and long-term preservation.

- Memory resources: Sufficient memory is the key to ensuring the efficient operation of the model, which can quickly load and process data and avoid performance degradation due to insufficient memory.

2. Software requirements

- Operating system: Choose a stable and compatible operating system, such as Linux, to support the operation of various AI frameworks and tools.

- AI framework: Select a suitable deep learning framework according to the model requirements, such as TensorFlow, PyTorch, etc., and ensure that the framework version is compatible with the model code.

- Development tools: Equipped with necessary development tools and libraries to facilitate model debugging, optimization and deployment.

3. Data requirements

- Data quality: Ensure the quality of training data, including data accuracy, completeness and consistency, to improve the performance and reliability of the model.

- Data security: During data processing, data security and privacy protection regulations must be strictly observed, and encryption, desensitization and other measures must be taken to protect user data.

- Data management: Establish a sound data management system, including data storage, backup, recovery and other mechanisms, to ensure data traceability and availability.

4. Security and compliance requirements

- Model security: Take measures to prevent models from being attacked or maliciously tampered with, such as model encryption and access control.

- Compliance: Follow relevant laws, regulations and industry standards to ensure the legality of model deployment and use, such as data protection regulations and industry specifications.

5. Network requirements

- Network bandwidth: Ensure sufficient network bandwidth to quickly transmit data and model files, especially during model training and inference, to avoid performance impacted by network delays.

- Network stability: Ensure network stability and reduce the impact of network failures on model deployment and operation.