Today, AI language large models have become the key to future development. Domestic and foreign technology companies have begun to independently develop exclusive large models.
What is a large language model? It is an autonomous learning algorithm that has various functions such as summarizing, translating, and generating text. It can create copywriting content independently without human control. Compared with traditional algorithm models, large language models are more inclined to use learning to master a systematic knowledge and apply it to various work tasks to maximize its benefits.
How to apply large language models to various industries? The answer is to build a large model of the domain. Domain large models refer to large language models that can assist in domain data annotation and model fine-tuning in enterprise applications. The current common operating model in the market is a large model framework based on large enterprises. Companies in various vertical fields can freely choose and adjust models that meet their own needs. On this basis, we can summarize the steps for enterprises to train their own large models.
1. Choose a suitable basic large model
Enterprises should establish a systematic indicator system based on their own business operations, such as accuracy, interpretability, stability, cost, etc. After quantifying the indicators, analyze and compare the characteristics of each model.
Take the BenTsao project as an example. When the project was first established, developers needed to create an authoritative medical knowledge map and collect relevant medical literature. And leverage the ChatGPT API to build a fine-tuned dataset. Fine-tune the instructions to achieve the effect of medical knowledge question and answer. Of course, when enterprises select models, they must also consider the basic capabilities and programming capabilities of the model itself. The basic capabilities of the model itself need to be strong enough, not precisely modulated. Because when enterprises develop, they often develop based on the basic capabilities of the model. Currently better models include Code LLaMA (34B) and Starcoder (15B).
2. Clean and label data
This is a key link related to the final operation. Data cleaning will affect the effect of model presentation. Data cleaning is performed in order, with the following main steps:
- Basic cleaning: remove duplicate recorded information, correct low-level errors, and ensure unified data format for easy viewing;
- Structured cleaning: On the basis of unified format, data is transformed and created, and model performance can be selected and improved;
- Content cleaning: Semantic identification, merging, and outlier processing of data can be performed.
- Advanced cleaning: Data synthesis can be performed through technical means, and complex data information such as images and drinks can be processed in addition to text information, while ensuring user privacy. This program is limited to specific applications.
- Audit and verification: Hire industry experts to conduct an audit to verify whether the quality of data cleaning is up to standard. This process involves many inspection standards and control processes.
Data annotation is the key to directly determining the direction of data collection and training in the early stage of model design. Data annotation can be divided into 9 steps: Determine the task and annotation requirements - Collect original data information - Clean and preprocess the data - Design the corresponding plan - Carry out data annotation - Control the quality and accuracy - Expand and enhance the data - establish corresponding training plans, verify and test the results - maintain a working method of continuous supervision and updating.
Among them, when we collect original data, we can collect public information provided by academic research institutions or enterprises to facilitate the field application of model training and evaluation. During the process, attention should be paid to the legal compliance of the data. In some cases, entity annotation, emotional annotation and grammatical annotation can also be performed.
3. Training and fine-tuning
Training is the process of deep learning on a large model to develop a model that can understand and generate natural language text. During this period, enterprises need to process and collect large-scale text data, and learn its inherent laws, semantics, and internal relationships between the context and context of the text. At present, the main training routes in the domestic market are TPU + XLA + TensorFlow led by Google and GPU + PyTorch + Megatron-LM + DeepSpeed controlled by NVIDIA, Meta, Microsoft and other major manufacturers.
Fine-tuning is to control the model to be trained based on the annotated data of a specific task. The main purpose of this stage is to modify the output layer and adjust the appropriate parameters while the model ore price remains unchanged, so that the model can adapt to the specific task. .
The final evaluation and iteration, deployment and monitoring focus on after-sales upgrades and real-time monitoring after model development. In these two links, developers need to evaluate the performance of the model according to the standards in the field. They can hire professionals to give evaluation suggestions, and the developers will then make improvements and iterative updates based on the evaluation.
After the model runs normally, developers also need to monitor and deploy the daily operation of the model.
Throughout the training process, the API plays a huge role. It can help developers process data efficiently and cost-effectively. It can also dynamically update model data while ensuring that private data can be safely accessed into large models.
- HBase : [HBase] service is a high-performance, highly scalable big data storage and retrieval solution based on the core technology of Apache HBase, an open source distributed column database system. It is designed to provide big data analysis, real-time Enterprise-level applications in various business scenarios such as data processing, Internet of Things (IoT), log management, and financial risk control provide efficient and reliable data management capabilities.
- Log Service : Cloud Log Service (CLS) is a one-stop log service platform provided by Tencent Cloud. It provides multiple services from log collection, log storage to log retrieval, chart analysis, monitoring alarms, log delivery and other services to assist Users use logs to solve multiple functions such as business operation and maintenance and service monitoring. In addition, Tencent Cloud CLS adopts a highly available distributed architecture design and performs multiple redundant backup storage of log data to prevent data from being unavailable due to single-node service downtime, providing service availability of up to 99.9%, and providing stable and reliable services for log data. Assure.
- Cloud Monitor : Cloud Monitor supports setting indicator threshold alarms for cloud product resources and custom reported resources. Provide you with three-dimensional cloud product data monitoring, intelligent data analysis, real-time abnormal alarms and visual data display. With second-level collection covering all indicator data, you can experience the most granular indicator changes and provide a refined cloud product monitoring experience. Cloud monitoring provides 24-hour free storage of second-level monitoring data and supports online viewing and data downloading.