🔥 Generate Reasoning Data Using AI Agents | Train LLMs with Chain-of-Thought Reasoning
Learn how to enhance your language models with reasoning capabilities using AI agents! In this tutorial, we'll show you how to automatically generate synthetic reasoning data and upload it to Hugging Face.
Code: https://docs.praison.ai/features/gene...
Massed compute: https://bit.ly/mervin-praison
Coupon: MervinPraison (50% Discount on Selected GPU)
🚀 What You'll Learn:
How to generate question-answer pairs with AI agents
Creating Chain-of-Thought reasoning steps automatically
Uploading datasets to Hugging Face
Training models with reasoning capabilities
Using Python and various AI tools effectively
💻 Required Packages:
pip install "PraisonAIAgents[llm]" llm-datasets huggingface-hub pandas
🔧 Tools & Requirements:
OpenAI API Key
Hugging Face Token
Python Environment
GPT-4o mini (or alternative models like Ollama)
💡 Key Features:
Automated data generation
Question-answer evaluation
Reasoning steps generation
Direct Hugging Face integration
Structured output handling
Custom topic support
Local model compatibility
🔗 Resources:
Hugging Face: https://huggingface.co
OpenAI: https://openai.com
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#AIAgents #MachineLearning #DeepLearning #Python #HuggingFace #OpenAI #Programming #DataScience #ArtificialIntelligence #LLM #Tutorial
0:00 - Introduction to adding reasoning capability to models
0:16 - Overview of using AI agents to generate data
0:38 - Explanation of the AI agents workflow
1:20 - Why use AI agents for automation
1:46 - Installation and setup instructions
2:24 - Creating the Python code structure
2:48 - Step 1: Creating tools for data handling
3:24 - Step 2: Setting up the four AI agents
4:09 - Step 3: Creating tasks for agents
5:41 - Step 4: Starting the agents
6:27 - Demonstration of the code execution
7:38 - Uploading dataset to Hugging Face
8:12 - Conclusion
