✨ Why Fine-Tuning Matters:
Tired of AI hallucinations? 😵💫 Fine-tuning is like training your GPT model for a specific job. It reduces errors and speeds up response times! 🏎️
🛠️ Setting Up:
- Grab the Code: Get the project files here: [Link to code] 💻
- Install Dependencies: Make sure you have
openai,tiktoken, andjsoninstalled. - Get Your OpenAI API Key: Head over to platform.openai.com 🔑
- Choose Your Model: Use GPT-4, GPT-4-mini, or even GPT-3.5 Turbo.
🧪 Creating Synthetic Data:
- Use the Power of GPT: Ask ChatGPT to create training data in JSONL format. Provide a system prompt example and specify the desired output structure. 🧠
- Get Specific: Tell ChatGPT what kind of scenarios to consider. For example, if you’re building a chatbot for a gym, ask it to create data for different customer inquiries.🏋️♀️
- JSONL Format is Key: Make sure there are no commas between elements in your JSONL file.
🔥 Fine-Tuning Time:
- Run the Script: Execute the Python script to start the fine-tuning process. 🚀
- Keep an Eye on the Loss: A lower training loss means better performance. Aim for a number close to 0.1 or lower. 📉
- Test, Test, Test: Once the fine-tuning is complete, test your model with different inputs and see how it performs! 🎉
🧰 Toolbox:
- OpenAI Fine-Tuning Cookbook: [Link to cookbook] – Learn best practices for data preparation and fine-tuning.
- Knotie-AI Sales Agent Project: [Link to project] – Get the complete code for the project demonstrated in the video.
- Knotie-AI Community: [Link to community] – Join the community for help and support.
🎉 Conclusion:
Fine-tuning unlocks the true potential of GPT models! By following this guide, you can create a powerful AI assistant tailored to your specific needs.
Challenge Time! Try fine-tuning GPT for a niche task and see the amazing results. What will you create? 🤔
