Insufficient memory on Huawei mobile phones has become a common problem faced by many users, with the increase in mobile applications and media files. To help users make full use of the storage space of their mobile phones, this article will introduce some practical methods to solve the problem of insufficient memory on Huawei mobile phones. 1. Clean cache: history records and invalid data to free up memory space and clear temporary files generated by applications. Find "Storage" in the settings of your Huawei phone, click "Clear Cache" and select the "Clear Cache" button to delete the application's cache files. 2. Uninstall infrequently used applications: To free up memory space, delete some infrequently used applications. Drag it to the top of the phone screen, long press the "Uninstall" icon of the application you want to delete, and then click the confirmation button to complete the uninstallation. 3.Mobile application to

The familiar open source large language models such as Llama3 launched by Meta, Mistral and Mixtral models launched by MistralAI, and Jamba launched by AI21 Lab have become competitors of OpenAI. In most cases, users need to fine-tune these open source models based on their own data to fully unleash the model's potential. It is not difficult to fine-tune a large language model (such as Mistral) compared to a small one using Q-Learning on a single GPU, but efficient fine-tuning of a large model like Llama370b or Mixtral has remained a challenge until now. Therefore, Philipp Sch, technical director of HuggingFace

According to a TrendForce survey report, the AI ??wave has a significant impact on the DRAM memory and NAND flash memory markets. In this site’s news on May 7, TrendForce said in its latest research report today that the agency has increased the contract price increases for two types of storage products this quarter. Specifically, TrendForce originally estimated that the DRAM memory contract price in the second quarter of 2024 will increase by 3~8%, and now estimates it at 13~18%; in terms of NAND flash memory, the original estimate will increase by 13~18%, and the new estimate is 15%. ~20%, only eMMC/UFS has a lower increase of 10%. ▲Image source TrendForce TrendForce stated that the agency originally expected to continue to

Local fine-tuning of DeepSeek class models faces the challenge of insufficient computing resources and expertise. To address these challenges, the following strategies can be adopted: Model quantization: convert model parameters into low-precision integers, reducing memory footprint. Use smaller models: Select a pretrained model with smaller parameters for easier local fine-tuning. Data selection and preprocessing: Select high-quality data and perform appropriate preprocessing to avoid poor data quality affecting model effectiveness. Batch training: For large data sets, load data in batches for training to avoid memory overflow. Acceleration with GPU: Use independent graphics cards to accelerate the training process and shorten the training time.

1. First, enter the Edge browser and click the three dots in the upper right corner. 2. Then, select [Extensions] in the taskbar. 3. Next, close or uninstall the plug-ins you do not need.

The AIxiv column is a column where this site publishes academic and technical content. In the past few years, the AIxiv column of this site has received more than 2,000 reports, covering top laboratories from major universities and companies around the world, effectively promoting academic exchanges and dissemination. If you have excellent work that you want to share, please feel free to contribute or contact us for reporting. Submission email: liyazhou@jiqizhixin.com; zhaoyunfeng@jiqizhixin.com. Recently, large-scale AI models such as large language models and Vincentian graph models have developed rapidly. Under this situation, how to adapt to rapidly changing needs and quickly adapt large models to various downstream tasks has become an important challenge. Limited by computing resources, traditional full-parameter micro

LaravelEloquent Model Retrieval: Easily obtaining database data EloquentORM provides a concise and easy-to-understand way to operate the database. This article will introduce various Eloquent model search techniques in detail to help you obtain data from the database efficiently. 1. Get all records. Use the all() method to get all records in the database table: useApp\Models\Post;$posts=Post::all(); This will return a collection. You can access data using foreach loop or other collection methods: foreach($postsas$post){echo$post->

Go function documentation contains warnings and caveats that are essential for understanding potential problems and avoiding errors. These include: Parameter validation warning: Check parameter validity. Concurrency safety considerations: Indicate the thread safety of a function. Performance considerations: Highlight the high computational cost or memory footprint of a function. Return type annotation: Describes the error type returned by the function. Dependency Note: Lists external libraries or packages required by the function. Deprecation warning: Indicates that a function is deprecated and suggests an alternative.