Summary
The video showcases the process of generating a dataset for fine-tuning with Chain of Thought reasoning. It introduces Camel AI and Fine-Tuning LLMS tools for data preparation and model training. The workflow includes setting up the model and data generator, creating a question-answer dataset, and explaining supervised fine-tuning for model optimization. The video details the training process, outcomes, testing, and deployment using the Hugging Face AI platform.
Introduction to Fine-Tuning
Showing how to generate a dataset for fine-tuning with Chain of Thought reasoning.
Data Formatting and Model Training
Preparing the data for model training and fine-tuning.
Tools for Fine-Tuning
Introducing Camel AI and Fine-Tuning LLMS tools for the process.
Workflow Overview
Overview of the workflow including model training on Chain of Thought data.
Model Setup with Chain of Thought Generator
Setting up the model and data generator for Chain of Thought reasoning.
Data Generation for Chain of Thought
Generating a question and answer dataset for the model training.
Supervised Fine-Tuning
Explaining the process of supervised fine-tuning for model optimization.
Model Tuning Trainer Setup
Setting up the model tuner for training process with specific parameters.
Training Process and Results
Detailed process of model training and the outcome of the training session.
Model Testing and Deployment
Testing the model and deploying it using Hugging Face AI platform.
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