打造并部署自己的GPT(比原版更强?!)
Table of Contents
- Introduction
- Extending a Large Language Model
- Teaching via Embeddings
- Fine-Tuning
- Choosing an LLM Model
- Hugging Face Open LLM Leaderboard
- Selecting Falcon
- Creating the Dataset
- Gathering YouTube Titles from Mr Beast's Channel
- Generating Descriptions using Chat GPT
- Utilizing dotTT for Faster Workflow
- Fine-Tuning the LLM Model
- Using Google Colab to Fine-Tune the Model
- Training the Model with the Dataset
- Testing Prompts with the Fine-Tuned Model
- Deploying and Customizing the Model
- Deploying the Model on Hugging Face
- Customizing the Collab File for Personalized Fine-Tuning
How to Extend and Fine-Tune Large Language Models
Let's say You've been using a large language model, and now you're interested in extending it. You want to teach it new information or make it capable of doing something new. When it comes to extending a large language model, you have two main options: teaching it via embeddings or fine-tuning it. In this article, we will explore the process of fine-tuning a large language model to perform a unique and interesting task.
1. Introduction
Language models have become an integral part of many applications and systems. They are designed to understand and generate human-like text, making them perfect for various tasks such as natural language processing, chatbots, content generation, and more. However, there are times when a pre-trained language model needs to be extended to perform new and specific tasks.
2. Extending a Large Language Model
To extend a large language model, there are two primary approaches: teaching via embeddings and fine-tuning. Let's take a closer look at each approach:
Teaching via Embeddings
Teaching a language model via embeddings involves providing the model with new data and information. This can include company financial reports, private repositories, or any other specific dataset that is not publicly available. By giving the model access to new data through embeddings, it can learn from the additional information while retaining its Current behavior and capabilities.
Pros:
- Provides access to new data and information
- Does not change the model's existing behavior
Cons:
- Limited to the data provided through embeddings
Fine-Tuning
Fine-tuning a language model involves changing the model's behavior to fit a specific task or generate a desired output. In this approach, the model is trained on a specialized dataset that includes examples and prompts related to the desired task. By providing specific instructions and examples, the model learns to respond in a particular way, enabling it to mimic a person or perform a specific function.
Pros:
- Can modify the model's behavior
- Allows for tailored responses and outputs
Cons:
- May require substantial training data for optimal results
3. Choosing an LLM Model
Before extending a language model, you need to select a suitable pre-trained model. The Hugging Face Open LLM Leaderboard is an excellent resource for discovering and comparing various open-source language models. One popular model is Falcon, available in both 7 billion and 40 billion versions. The smaller version is often more convenient for fine-tuning and deployment purposes.
Pros:
- Wide selection of open-source language models
- User ratings and performance metrics available on the leaderboard
Cons:
- It can be challenging to choose the most appropriate model for a specific task
4. Creating the Dataset
To fine-tune a language model, you need a dataset that aligns with your desired task. In this example, we aim to generate YouTube titles inspired by Mr Beast. The process involves gathering a set of existing video titles from Mr Beast's channel and generating corresponding descriptions using chat GPT or a similar language model. With a comprehensive dataset in place, we can proceed with fine-tuning the chosen LLM model.
Pros:
- Opportunity to curate custom data for a specific task
- Flexibility in generating a dataset that fits your needs
Cons:
- Requires manual or automated generation of descriptions for the dataset
5. Fine-Tuning the LLM Model
To fine-tune the selected language model, we will utilize Google Colab, a platform that allows us to execute code on powerful machines. By following a straightforward script in the provided Collab file, we can load the pre-trained model, set up the new model for fine-tuning, load the dataset, and initiate the training process. The script allows us to Visualize the training progress and test the fine-tuned model with sample prompts.
Pros:
- Efficient utilization of powerful computing resources
- Clear visualization of training progress and results
Cons:
- Fine-tuning may be time-consuming on lower-powered machines
6. Deploying and Customizing the Model
Once satisfied with the fine-tuned model, it can be deployed and accessed through an API. Hugging Face provides an easy-to-use platform for deploying models and making them accessible via API calls. By customizing the Collab file with your own dataset and prompt specifications, you can fine-tune the model to suit your specific requirements.
Pros:
- Quick and simple deployment of the fine-tuned model
- Customization options to fit specific needs
Cons:
- Requires familiarity with API usage and deployment procedures
Highlights:
- Extending language models through embeddings or fine-tuning
- Choosing a pre-trained language model with Hugging Face
- Creating a dataset tailored to the desired task
- Fine-tuning the selected language model using Google Colab
- Deploying and customizing the fine-tuned model with Hugging Face's API