Detailed explanation of OGAI: How the AIStation scheduling platform achieves efficient and long-term continuous training of large models - Code World

Detailed explanation of OGAI: How the AIStation scheduling platform achieves efficient and long-term continuous training of large models

Mobile 2023-09-20 17:14:38 views: null

NoSuchKey

Guess you like

Origin my.oschina.net/u/5547601/blog/10112280

Detailed explanation of OGAI: How the AIStation scheduling platform achieves efficient and long-term continuous training of large models

Detailed explanation of OGAI: How the AIStation scheduling platform achieves efficient and long-term continuous training of large models

Detailed explanation of OGAI: How the AIStation scheduling platform achieves efficient and long-term continuous training of large models

Detailed explanation of OGAI: How the AIStation scheduling platform achieves efficient and long-term continuous training of large models

Continuous pre-training of large language models

Practical application of large models 10 - Detailed explanation of large model domain knowledge and parameter efficient fine-tuning (PEFT) technology, and use PEFT to train your own large models

How to build an efficient high-performance computing large model training platform in the SAM era

SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Pre-training of large language models [6]: Detailed explanation of the definition principle of Chain-of-thought (CoT), Zero-shot CoT, Few-shot CoT and application on LLM

Long-term scheduling, medium-term scheduling, short-term scheduling

Detailed explanation of thread scheduling and process scheduling

Efficient fine-tuning technology for large models

Detailed explanation of test tracking operations of the one-stop open source continuous testing platform MerterSphere

OpenAI Development Series (2): Development History of Large Language Models and Detailed Explanation of Transformer Architecture

Easy-to-understand machine learning - detailed explanation of training models using the server

Pre-training of large language models [2]: GPT, GPT2, GPT3, GPT3.5, GPT4 related theoretical knowledge and model implementation, model application and detailed explanation of the differences between versions

Continual Pre-Training of Large Language Models: How to (re)warm your model?

Hong Kong Securities: How to make long-term investment in stocks? What are the advantages of long-term investing?

Pre-training large models and financial quantification

Google: Large models not only have the ability to emerge, but also have the ability to "comprehend" after a long training time!

Linux process thread and scheduling detailed explanation

Detailed explanation of Java timing task scheduling

Summary of three fine-tuning techniques for pre-training large language models: fine-tuning, parameter-efficient fine-tuning and prompt-tuning

LLM pre-training large language models Pre-training large language models

From 0 to 1! How Dewu builds a general large model training and reasoning platform

Efficient fine-tuning of large models - introduction to the PEFT framework

How database supports unlimited data growth? (Long-term update)

Depth learning how to interact with the server elegance? (Long-term update)

How to do medium and long-term planning for agile development projects

How does the applet apply for a long-term subscription (device message)?

Recommended

Ranking

jquery countdown timer

Front-end high-frequency interview questions Day02

c# uses MQTT communication

[Linux development—I/O reuse]

Ten misunderstandings of using docker

Is it thread-safe to synchronize only on add to HashSet?

Hutool tool to send email send Email

docker data volume, docker data persistence

Cannot connect to localhost API from Android app

matplotlib basic plus advanced

Daily

More

2025-01-11(0)

2025-01-10(0)

2025-01-09(0)

2025-01-08(0)

2025-01-07(0)

2025-01-06(0)

2025-01-05(0)

2025-01-04(0)

2025-01-03(0)

2025-01-02(0)