教你如何训练自己的大模型知识库｜ huggingface | llama ｜langchain ｜faiss | 零成本 ｜ google colab
TLDRThe video script discusses the process of training a large AI model using a framework like Langchain and tools from Hugging Face and Meta. It explains the steps from data preparation, including text segmentation and vectorization, to model selection and integration. The video highlights the use of Google Colab for cost-effective experimentation and provides a detailed guide on training a model to generate contextually relevant responses. The example of training the model to correctly interpret '1688' as an e-commerce platform in China is used to demonstrate the effectiveness of the process.
Takeaways
- 📅 The speaker took a break during the Spring Festival and utilized the time to research the newly released Sora model by Open AI, which generates videos from text.
- 🌟 The Sora model's release has been a groundbreaking moment that captured global attention.
- 🛠️ The process of training a large model involves three key components: training documents (data), the large model itself, and a framework to connect the two, such as LangChain.
- 📈 The training data must be processed through segmentation, chunking, and vectorization to be suitable for input into large models, which do not support very long text inputs.
- 🔍 Vectorized data is stored in a vector space to prepare it as a data source for when a query is input and needs to be matched with the stored data.
- 📚 Hugging Face is introduced as a valuable resource for finding and using open-source models, with its Transformers library simplifying the usage of various models.
- 🔑 For using certain models, like one from Meta (Facebook), an application for authorization is required, but the process is described as easy.
- 💡 The LangChain framework and Faiss library are highlighted for data storage and integration with large models, facilitating the training process.
- 👨💻 Google Colab is recommended as a tool for试验ing the model training process at no cost, providing a free and easy-to-use platform with GPU resources.
- 🔧 The video script provides a step-by-step guide on how to install dependencies, load data, and train a model using NoteBook, showcasing the practical application of the discussed concepts.
- 🚀 The end goal of training a large model is to tailor it to specific business scenarios or professional fields, enhancing its utility and effectiveness in those contexts.
Q & A
What was the main reason for the speaker's break from video creation?
-The speaker took a break due to the Spring Festival, using the time for rest.
What significant AI development was mentioned in the video?
-OpenAI released a model called Sora, which is capable of generating videos from text.
What is the primary component of the large model training process?
-The primary components include training data, the large model itself, and a framework to connect the data and model, such as Langchain.
Why is text segmentation necessary in the training process?
-Text segmentation is necessary because existing large models do not support very long text inputs, so the text needs to be divided into smaller, more suitable blocks for input.
What does 'vectorization' of data refer to in the context of the script?
-Vectorization refers to the process of encoding and converting text data into a format that can be recognized and processed by large models, as computers understand binary code.
Where can one find a suitable large model for their project?
-Hugging Face is a platform where many companies and individuals upload their models, making it a good place to find a suitable large model.
What is the role of the 'transformers' library from Hugging Face?
-The 'transformers' library simplifies the process of using various models available on Hugging Face by providing an easy-to-use interface for developers.
How does Google Colab help in the training process?
-Google Colab is a free tool that provides a platform to experiment with models without incurring high costs, as it offers free access to GPU resources.
What is the purpose of the FAISS library mentioned in the script?
-FAISS is a library for efficient similarity search and clustering of dense vectors, which is used for storing and searching vectorized text data in the training process.
How does the training process improve the model's performance?
-By training the model with specific data, such as information from a website, the model learns to generate more accurate and relevant responses tailored to the given context or domain.
What was the final outcome of the training example provided in the script?
-The final outcome was that the model was able to correctly identify '1688' as a mainland Chinese e-commerce platform after being trained with specific data.
Outlines
📚 Introduction to Large Model Training
This paragraph introduces the concept of large model training, highlighting the release of the Sora model by Open AI during the Spring Festival. The speaker shares their experience with studying the training process of large models. The paragraph outlines the essential components of large model training, including training data, the model itself, and the framework that connects them, such as LangChain. It explains the process of data input, text segmentation, chunking, vectorization, and the use of a vector database for input queries. The paragraph emphasizes the importance of selecting an appropriate model and the role of vector storage and search in the training process.
🔍 Tools and Frameworks for Model Training
This paragraph delves into the practical aspects of large model training, discussing the use of Hugging Face's Transformers library for model selection and the integration of training data. It explains the process of using Hugging Face's Transformers to call upon large models and the importance of loading training data, which can be sourced from websites and processed using LangChain tools. The paragraph also introduces FAISS, a vector data storage library, and explains how it works in conjunction with LangChain to create a context-aware question and generate a prompt for the model. The speaker guides the audience through the entire training process, from data collection to model output.
🚀 Practical Training and Zero-Cost Experimentation
In this paragraph, the speaker discusses the practical steps for training a large model and the tools available for zero-cost experimentation, such as Google Colab. It explains how Colab provides a free and easy-to-use platform for testing models, with the ability to share notebooks with others. The paragraph outlines the process of installing dependencies, calling upon Hugging Face's Transformers library, and loading the model and tokenizer. It also covers the integration of the large model into the LangChain framework and the process of training the model with specific data. The speaker demonstrates the training process with an example, showing how the model can be fine-tuned to provide desired outputs, such as correctly identifying 1688 as a Chinese e-commerce platform.
Mindmap
Keywords
💡Sora model
💡AI training
💡Langchain framework
💡Text segmentation
💡Text vectorization
💡Hugging Face
💡Transformers library
💡Faiss
💡Google Colab
💡AI model selection
💡Contextual understanding
Highlights
The speaker took a break during the Spring Festival and used the time to study the newly released Sora model by Open AI, which generates videos from text.
The Sora model's release has shocked the world due to its innovative capabilities in text-to-video generation.
The speaker shares insights from their research on large model training and guides the audience through the process of training their own model.
The training process involves three key components: training documents (data), the large model itself, and a framework that connects data and model, such as LangChain.
Text is split and chunked to accommodate the limitations of large models, which do not support very long text inputs.
Data is then vectorized to be recognized by the large model, as computers understand binary code, and text must be encoded and vectorized.
Vectorized data is stored in a vector space, preparing the data source for input queries.
Queries are also vectorized and compared with data in the vector database to form associated text blocks and generate prompts for the large model.
A complete training model chain is established through this process, which includes data preparation, query comparison, and result generation.
The selection of a suitable large model is crucial, and Hugging Face's Transformers library simplifies the process by providing easy access to various models.
The speaker introduces the use of LangChain for data storage and integration with large models, facilitating model training.
Faiss, an open-source library by Facebook (Meta), is used for vector data storage.
Google Colab is recommended as a zero-cost tool for experimenting with the training process, providing free access to GPU resources.
The speaker demonstrates how to use Hugging Face's Transformers to call a large model and integrate it into a LangChain framework for training purposes.
The process of loading data, vectorizing it, and storing it in Faiss is detailed, showing how to prepare the data for model training.
The speaker shows how to fine-tune the model with specific data, such as training it to correctly identify '1688' as a Chinese e-commerce platform.
The video concludes with the speaker's promise to share all reference materials and invites the audience to engage in discussions in the comments section.