Feed Your OWN Documents to a Local Large Language Model!
Science & Technology
Introduction
In today's digital landscape, the ability to augment large language models (LLMs) with your own knowledge files and documents has become a hot topic. In this article, we'll dive into how to enhance LLMs—both locally and online—by integrating your own information. We'll cover the differences between three primary methods: retraining a model, utilizing Retrieval Augmented Generation (RAG), and simply uploading documents for reference in the context window.
Introduction: Why Enhance Your Language Model?
Before we explore the technical steps, let's begin with a demonstration of how a modestly sized model performs locally on a powerful Dual Nvidia RTX 6000 setup. In previous discussions, we've explored using gigantic models with immense computing power, but this time, we'll see how efficiently a 1 billion parameter model operates.
Benchmarking the 1 Billion Parameter Model
Using a model like Llama 3.2, which offers 1 billion parameters, we can assess how quickly it generates responses. With a test query asking it to narrate a story, we find it successfully produces 345 tokens per second initially, settling at 324 tokens per second after generating a longer narrative. Comparatively, a larger model with 70 billion parameters produces around 20 tokens per second—a significant difference but still usable for various applications.
Methods to Add Knowledge to LLMs
1. Retraining a Model
When we think about retraining a model, it's akin to sending a student back to school. They learn new information alongside everything they've already mastered. This comprehensive process encompasses updating with new data, requiring substantial computational resources, time, and expertise. While retraining lasts forever with the model retaining all the updates, it might not be feasible for many users due to hardware and software limitations.
2. Retrieval Augmented Generation (RAG)
Unlike the previous method, RAG allows our model to dynamically retrieve information instead of permanently integrating new knowledge. Picture a student who references a library for the latest data. When a question arises, the model quickly pulls relevant information to construct its answer. This approach is agile and favorable for situations where information changes frequently; it doesn't burden the model with outdated data.
3. Uploading Documents into the Context Window
This method resembles providing a cheat sheet during an exam. The model can consult uploaded documents to answer questions but won’t remember the documents beyond the current session. While this is efficient for quick reference, it’s less accommodating for extensive datasets since the model processes everything provided, regardless of its relevance.
Uploading Documents to ChatGPT
Using ChatGPT, for instance, users can upload files for context. In practice, when we upload a relevant document, such as a user manual for a PDP-1134, the model can deliver specific answers grounded in the provided context. However, performing this for multiple documents can be unwieldy.
To speed things up, creating a custom ChatGPT instance dedicated to specific topics enhances usability. After naming and defining the custom model, users can upload several files, making them available for any queries related to that domain.
Setting Up with OpenLLaMA and Open Web UI
For those running their models locally via OpenLLaMA and Open Web UI, the process remains similar. Users first upload their context files, like the PDP-1134 manual. The model can then reference those documents during inquiries, allowing for precise responses backed by specific citations from the uploaded material.
Conclusion: Selecting the Right Approach
As we’ve explored, the method of feeding knowledge into LLMs depends on your specific needs. Retraining provides a deep, permanent understanding of data, RAG offers adaptability, and uploading documents serves well in quick need scenarios. The choice hinges on your requirements for permanence, flexibility, and the scale of the knowledge base.
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Keyword
- Large Language Model (LLM)
- Retraining
- Retrieval Augmented Generation (RAG)
- Context Window
- Uploading Documents
- ChatGPT
- OpenLLaMA
- Open Web UI
FAQ
Q1: Can I retrain a language model using my own data?
A1: Retraining is feasible, but requires significant hardware resources, programming skills, and can take time. It’s often not practical for most users.
Q2: What is Retrieval Augmented Generation (RAG)?
A2: RAG is a method allowing models to dynamically retrieve data from a collection of documents or databases rather than permanently embedding new information.
Q3: How do I upload documents to ChatGPT for instant reference?
A3: You can simply use the upload button to add your documents, which the model can refer to while answering your questions during the same session.
Q4: Does the model retain the uploaded documents for future sessions?
A4: No, the model does not retain any uploaded documents once the session ends, and you'll need to upload them again for future use.
Q5: Can I create a custom version of ChatGPT to answer specialized questions?
A5: Yes, you can create a custom ChatGPT model, upload necessary documents, and have the model answer queries based on the content of those documents.