Supervised Fine-Tuning (SFT)

Supervised Fine-Tuning (SFT) is a technique used to adapt a pre-trained Large Language Model (LLM) to a specific downstream task using labeled data. Let’s break it down:

Pre-Trained LLM:
- Initially, we have a pre-trained language model (like GPT-3 or Phi-3) that has learned from a large corpus of text data.
- This pre-trained model has already acquired knowledge about language, grammar, and context.
Adapting to a Specific Task:
- To make the model useful for a specific task (e.g., answering questions, generating code, or translating text), we fine-tune it.
- Fine-tuning involves training the model further on a smaller dataset specific to the task.
Labeled Dataset:
- We provide the model with a labeled dataset.
- Each example in this dataset consists of an input (e.g., a prompt or question) and its corresponding correct output (label).
Training Process:
- During fine-tuning, the model learns to predict the correct label for each input.
- It adjusts its parameters based on the labeled examples, effectively adapting its knowledge to the specific task.
SFTTrainer:
- The SFTTrainer class from libraries like Hugging Face’s Transformer Reinforcement Learning (TRL) facilitates the SFT process.
- It accepts a column in the training dataset CSV containing system instructions, questions, and answers (forming the prompt structure).
- Different models may require different prompt structures, but a standard approach is to use the dataset structure described in OpenAI’s InstructGPT paper.

In summary, SFT allows us to fine-tune a pre-trained model to perform well on a specific task by leveraging labeled data.

Comments

Transforming Workflows with CrewAI: Harnessing the Power of Multi-Agent Collaboration for Smarter Automation

CrewAI is a framework designed to implement the multi-agent concept effectively. It helps create, manage, and coordinate multiple AI agents to work together on complex tasks. CrewAI simplifies the process of defining roles, assigning tasks, and ensuring collaboration among agents. How CrewAI Fits into the Multi-Agent Concept 1. Agent Creation: - In CrewAI, each AI agent is like a specialist with a specific role, goal, and expertise. - Example: One agent focuses on market research, another designs strategies, and a third plans marketing campaigns. 2. Task Assignment: - You define tasks for each agent. Tasks can be simple (e.g., answering questions) or complex (e.g., analyzing large datasets). - CrewAI ensures each agent knows what to do based on its defined role. 3. Collaboration: - Agents in CrewAI can communicate and share results to solve a big problem. For example, one agent's output becomes the input for an...

Optimizing LLM Queries for CSV Files to Minimize Token Usage: A Beginner's Guide

When working with large CSV files and querying them using a Language Model (LLM), optimizing your approach to minimize token usage is crucial. This helps reduce costs, improve performance, and make your system more efficient. Here’s a beginner-friendly guide to help you understand how to achieve this. What Are Tokens, and Why Do They Matter? Tokens are the building blocks of text that LLMs process. A single word like "cat" or punctuation like "." counts as a token. Longer texts mean more tokens, which can lead to higher costs and slower query responses. By optimizing how you query CSV data, you can significantly reduce token usage. Key Strategies to Optimize LLM Queries for CSV Files 1. Preprocess and Filter Data Before sending data to the LLM, filter and preprocess it to retrieve only the relevant rows and columns. This minimizes the size of the input text. How to Do It: Use Python or database tools to preprocess the CSV file. Filter for only the rows an...

Artificial Intelligence (AI) beyond the realms of Machine Learning (ML) and Deep Learning (DL).

AI (Artificial Intelligence) : Definition : AI encompasses technologies that enable machines to mimic cognitive functions associated with human intelligence. Examples : 🗣️ Natural Language Processing (NLP) : AI systems that understand and generate human language. Think of chatbots, virtual assistants (like Siri or Alexa), and language translation tools. 👀 Computer Vision : AI models that interpret visual information from images or videos. Applications include facial recognition, object detection, and self-driving cars. 🎮 Game Playing AI : Systems that play games like chess, Go, or video games using strategic decision-making. 🤖 Robotics : AI-powered robots that can perform tasks autonomously, such as assembly line work or exploring hazardous environments. Rule-Based Systems : Definition : These are AI systems that operate based on predefined rules or logic. Examples : 🚦 Traffic Light Control : Rule-based algorithms manage traffic lights by following fix...

Tech GPT

Search This Blog